1636:; that is, in terms of repeated sampling from a population. However, the approach of Neyman develops these procedures in terms of pre-experiment probabilities. That is, before undertaking an experiment, one decides on a rule for coming to a conclusion such that the probability of being correct is controlled in a suitable way: such a probability need not have a frequentist or repeated sampling interpretation. In contrast, Bayesian inference works in terms of conditional probabilities (i.e. probabilities conditional on the observed data), compared to the marginal (but conditioned on unknown parameters) probabilities used in the frequentist approach.
55:
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782:: This term typically implies assumptions 'in between' fully and non-parametric approaches. For example, one may assume that a population distribution has a finite mean. Furthermore, one may assume that the mean response level in the population depends in a truly linear manner on some covariate (a parametric assumption) but not make any parametric assumption describing the variance around that mean (i.e. about the presence or possible form of any
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distributions that are nearly normal." In particular, a normal distribution "would be a totally unrealistic and catastrophically unwise assumption to make if we were dealing with any kind of economic population." Here, the central limit theorem states that the distribution of the sample mean "for very large samples" is approximately normally distributed, if the distribution is not heavy-tailed.
1017:
scheme. Seriously misleading results can be obtained analyzing data from randomized experiments while ignoring the experimental protocol; common mistakes include forgetting the blocking used in an experiment and confusing repeated measurements on the same experimental unit with independent replicates of the treatment applied to different experimental units.
754:: The probability distributions describing the data-generation process are assumed to be fully described by a family of probability distributions involving only a finite number of unknown parameters. For example, one may assume that the distribution of population values is truly Normal, with unknown mean and variance, and that datasets are generated by
1663:, which play the role of (negative) utility functions. Loss functions need not be explicitly stated for statistical theorists to prove that a statistical procedure has an optimality property. However, loss-functions are often useful for stating optimality properties: for example, median-unbiased estimators are optimal under
928:. The magnitude of the difference between the limiting distribution and the true distribution (formally, the 'error' of the approximation) can be assessed using simulation. The heuristic application of limiting results to finite samples is common practice in many applications, especially with low-dimensional
872:. Yet for many practical purposes, the normal approximation provides a good approximation to the sample-mean's distribution when there are 10 (or more) independent samples, according to simulation studies and statisticians' experience. Following Kolmogorov's work in the 1950s, advanced statistics uses
1025:
Model-free techniques provide a complement to model-based methods, which employ reductionist strategies of reality-simplification. The former combine, evolve, ensemble and train algorithms dynamically adapting to the contextual affinities of a process and learning the intrinsic characteristics of the
2682:
Kolmogorov (1963, p.369): "The frequency concept, based on the notion of limiting frequency as the number of trials increases to infinity, does not contribute anything to substantiate the applicability of the results of probability theory to real practical problems where we have always to deal with
1880:
Formulating the statistical model: A statistical model is defined based on the problem at hand, specifying the distributional assumptions and the relationship between the observed data and the unknown parameters. The model can be simple, such as a normal distribution with known variance, or complex,
1016:
It is standard practice to refer to a statistical model, e.g., a linear or logistic models, when analyzing data from randomized experiments. However, the randomization scheme guides the choice of a statistical model. It is not possible to choose an appropriate model without knowing the randomization
911:
describe the sample statistic's limiting distribution if one exists. Limiting results are not statements about finite samples, and indeed are irrelevant to finite samples. However, the asymptotic theory of limiting distributions is often invoked for work with finite samples. For example, limiting
1940:
Model checking: After obtaining the parameter estimates and assessing their uncertainty, it is important to assess the adequacy of the statistical model. This involves checking the assumptions made in the model and evaluating the fit of the model to the data using goodness-of-fit tests, residual
1884:
Constructing the likelihood function: Given the statistical model, the likelihood function is constructed by evaluating the joint probability density or mass function of the observed data as a function of the unknown parameters. This function represents the probability of observing the data for
995:
Objective randomization allows properly inductive procedures. Many statisticians prefer randomization-based analysis of data that was generated by well-defined randomization procedures. (However, it is true that in fields of science with developed theoretical knowledge and experimental control,
821:
can invalidate statistical inference. More complex semi- and fully parametric assumptions are also cause for concern. For example, incorrectly assuming the Cox model can in some cases lead to faulty conclusions. Incorrect assumptions of
Normality in the population also invalidates some forms of
968:
randomization allows inferences to be based on the randomization distribution rather than a subjective model, and this is important especially in survey sampling and design of experiments. Statistical inference from randomized studies is also more straightforward than many other situations. In
826:
parametric model is viewed skeptically by most experts in sampling human populations: "most sampling statisticians, when they deal with confidence intervals at all, limit themselves to statements about based on very large samples, where the central limit theorem ensures that these will have
967:
For a given dataset that was produced by a randomization design, the randomization distribution of a statistic (under the null-hypothesis) is defined by evaluating the test statistic for all of the plans that could have been generated by the randomization design. In frequentist inference, the
1593:
This paradigm calibrates the plausibility of propositions by considering (notional) repeated sampling of a population distribution to produce datasets similar to the one at hand. By considering the dataset's characteristics under repeated sampling, the frequentist properties of a statistical
2741:
Pfanzagl (1994): "The crucial drawback of asymptotic theory: What we expect from asymptotic theory are results which hold approximately . . . . What asymptotic theory has to offer are limit theorems."(page ix) "What counts for applications are approximations, not limits." (page
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The
Bayesian calculus describes degrees of belief using the 'language' of probability; beliefs are positive, integrate into one, and obey probability axioms. Bayesian inference uses the available posterior beliefs as the basis for making statistical propositions. There are
1888:
Maximizing the likelihood function: The next step is to find the set of parameter values that maximizes the likelihood function. This can be achieved using optimization techniques such as numerical optimization algorithms. The estimated parameter values, often denoted as
1744:
Formally, Bayesian inference is calibrated with reference to an explicitly stated utility, or loss function; the 'Bayes rule' is the one which maximizes expected utility, averaged over the posterior uncertainty. Formal
Bayesian inference therefore automatically provides
1944:
Inference and interpretation: Finally, based on the estimated parameters and model assessment, statistical inference can be performed. This involves drawing conclusions about the population parameters, making predictions, or testing hypotheses based on the estimated
1731:
Many informal
Bayesian inferences are based on "intuitively reasonable" summaries of the posterior. For example, the posterior mean, median and mode, highest posterior density intervals, and Bayes Factors can all be motivated in this way. While a user's
1736:
need not be stated for this sort of inference, these summaries do all depend (to some extent) on stated prior beliefs, and are generally viewed as subjective conclusions. (Methods of prior construction which do not require external input have been
770:: The assumptions made about the process generating the data are much less than in parametric statistics and may be minimal. For example, every continuous probability distribution has a median, which may be estimated using the sample median or the
1753:
sense. Given assumptions, data and utility, Bayesian inference can be made for essentially any problem, although not every statistical inference need have a
Bayesian interpretation. Analyses which are not formally Bayesian can be (logically)
2751:
Pfanzagl (1994) : "By taking a limit theorem as being approximately true for large sample sizes, we commit an error the size of which is unknown. Realistic information about the remaining errors may be obtained by simulations." (page
2128:, also known as a "fiducial distribution". In subsequent work, this approach has been called ill-defined, extremely limited in applicability, and even fallacious. However this argument is the same as that which shows that a so-called
1000:
are recommended by leading statistical authorities as allowing inferences with greater reliability than do observational studies of the same phenomena. However, a good observational study may be better than a bad randomized experiment.
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Different schools of statistical inference have become established. These schoolsâor "paradigms"âare not mutually exclusive, and methods that work well under one paradigm often have attractive interpretations under other paradigms.
1007:
However, at any time, some hypotheses cannot be tested using objective statistical models, which accurately describe randomized experiments or random samples. In some cases, such randomized studies are uneconomical or unethical.
732:
is a set of assumptions concerning the generation of the observed data and similar data. Descriptions of statistical models usually emphasize the role of population quantities of interest, about which we wish to draw inference.
2273:âthat future observations should behave like past observationsâcame to the attention of the English-speaking world with the 1974 translation from French of his 1937 paper, and has since been propounded by such statisticians as
1151:
786:). More generally, semi-parametric models can often be separated into 'structural' and 'random variation' components. One component is treated parametrically and the other non-parametrically. The well-known
813:
Whatever level of assumption is made, correctly calibrated inference, in general, requires these assumptions to be correct; i.e. that the data-generating mechanisms really have been correctly specified.
677:(or set estimate), i.e. an interval constructed using a dataset drawn from a population so that, under repeated sampling of such datasets, such intervals would contain the true parameter value with the
1365:
1873:. In likelihood-based inference, the goal is to find the set of parameter values that maximizes the likelihood function, or equivalently, maximizes the probability of observing the given data.
2176:
and applied this to linear models. The theory formulated by Fraser has close links to decision theory and
Bayesian statistics and can provide optimal frequentist decision rules if they exist.
2081:). However, MDL avoids assuming that the underlying probability model is known; the MDL principle can also be applied without assumptions that e.g. the data arose from independent sampling.
2020:: it offers an estimate of the relative information lost when a given model is used to represent the process that generated the data. (In doing so, it deals with the trade-off between the
2051:. The (MDL) principle selects statistical models that maximally compress the data; inference proceeds without assuming counterfactual or non-falsifiable "data-generating mechanisms" or
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1217:
2212:
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Rahlf, Thomas (2014). "Statistical
Inference", in Claude Diebolt, and Michael Haupert (eds.), "Handbook of Cliometrics ( Springer Reference Series)", Berlin/Heidelberg: Springer.
2009:
for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Thus, AIC provides a means for
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588:. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population. In
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it provides the MDL description of the data, on average and asymptotically. In minimizing description length (or descriptive complexity), MDL estimation is similar to
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The statistical analysis of a randomized experiment may be based on the randomization scheme stated in the experimental protocol and does not need a subjective model.
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Assessing uncertainty: Once the MLEs are obtained, it is crucial to quantify the uncertainty associated with the parameter estimates. This can be done by calculating
1871:
1549:
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Konishi & Kitagawa state, "The majority of the problems in statistical inference can be considered to be problems related to statistical modeling". Relatedly,
596:
is sometimes used instead to mean "make a prediction, by evaluating an already trained model"; in this context inferring properties of the model is referred to as
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2168:. Barnard reformulated the arguments behind fiducial inference on a restricted class of models on which "fiducial" procedures would be well-defined and useful.
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to be used, the absence of obviously explicit utilities and prior distributions has helped frequentist procedures to become widely viewed as 'objective'.
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Soofi, Ehsan S. (December 2000). "Principal information-theoretic approaches (Vignettes for the Year 2000: Theory and
Methods, ed. by George Casella)".
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is smooth. Also, relying on asymptotic normality or resampling, we can construct confidence intervals for the population feature, in this case, the
809:
The above image shows a histogram assessing the assumption of normality, which can be illustrated through the even spread underneath the bell curve.
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According to Peirce, acceptance means that inquiry on this question ceases for the time being. In science, all scientific theories are revisable.
6034:
2140:, it does not necessarily invalidate conclusions drawn from fiducial arguments. An attempt was made to reinterpret the early work of Fisher's
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relies on some regularity conditions, e.g. functional smoothness. For instance, model-free randomization inference for the population feature
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2764:(1934) "On the two different aspects of the representative method: The method of stratified sampling and the method of purposive selection",
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4954:
2732:. All they can do is suggest certain approaches whose performance must then be checked on the case at hand." â Le Cam (1986) (page xiv)
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randomized experiments may increase the costs of experimentation without improving the quality of inferences.) Similarly, results from
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Given the difficulty in specifying exact distributions of sample statistics, many methods have been developed for approximating these.
535:
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has said, "How translation from subject-matter problem to statistical model is done is often the most critical part of an analysis".
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Neyman, Jerzy. 1923 . "On the
Application of Probability Theory to AgriculturalExperiments. Essay on Principles. Section 9."
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2384:
1758:; a feature of Bayesian procedures which use proper priors (i.e. those integrable to one) is that they are guaranteed to be
4949:
4649:
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Bandyopadhyay & Forster (2011). See the book's
Introduction (p.3) and "Section III: Four Paradigms of Statistics".
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766:
632:. Given a hypothesis about a population, for which we wish to draw inferences, statistical inference consists of (first)
17:
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Likelihood-based inference is a paradigm used to estimate the parameters of a statistical model based on observed data.
2604:
3115:"Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering"
2406:
refers to the process of executing a TensorFlow Lite model on-device in order to make predictions based on input data.
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Barnard, G.A. (1995) "Pivotal Models and the Fiducial Argument", International Statistical Review, 63 (3), 309â323.
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Statistical inference makes propositions about a population, using data drawn from the population with some form of
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Tang, Ming; Gao, Chao; Goutman, Stephen; Kalinin, Alexandr; Mukherjee, Bhramar; Guan, Yuanfang; Dinov, Ivo (2019).
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The frequentist procedures of significance testing and confidence intervals can be constructed without regard to
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While statisticians using frequentist inference must choose for themselves the parameters of interest, and the
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Regression Analysis: A Constructive Critique (Advanced Quantitative Techniques in the Social Sciences) (v. 11)
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Lee, Se Yoon (2021). "Gibbs sampler and coordinate ascent variational inference: A set-theoretical review".
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1997:
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1489:, can be consistently estimated via local averaging or local polynomial fitting, under the assumption that
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In either case, the model-free randomization inference for features of the common conditional distribution
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ASA Guidelines for the first course in statistics for non-statisticians. (available at the ASA website)
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ASA Guidelines for the first course in statistics for non-statisticians. (available at the ASA website)
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estimators are optimal under squared error loss functions, in that they minimize expected loss.
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tends to infinity' are logically devoid of content about what happens at any particular
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2261:, but it fell out of favor in the 20th century due to a new parametric approach pioneered by
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3199:"Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability"
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4009:"Miracles and statistics: the casual assumption of independence (ASA Presidential Address)"
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proposition can be quantifiedâalthough in practice this quantification may be challenging.
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The evaluation of MDL-based inferential procedures often uses techniques or criteria from
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of the process that generates the data and (second) deducing propositions from the model.
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3066:"Randomization-based statistical inference: A resampling and simulation infrastructure"
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3008:"Randomization-based statistical inference: A resampling and simulation infrastructure"
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of the sample with the population; in randomized experiments, randomization warrants a
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De Finetti, Bruno (1937). "La Prévision: ses lois logiques, ses sources subjectives".
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4477:"Models and Statistical Inference: the controversy between Fisher and NeymanâPearson"
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However, if a "data generating mechanism" does exist in reality, then according to
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are typically used as a preliminary step before more formal inferences are drawn.
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De Finetti, Bruno (1992). "Foresight: Its Logical Laws, Its Subjective Sources".
2562:(2008) 62: 110-119. (Reprinted as Chapter 11 (pages 169â192) of Freedman (2010)).
2500:
2274:
2270:
2265:. The approach modeled phenomena as a physical system observed with error (e.g.,
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Predictive inference is an approach to statistical inference that emphasizes the
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The minimum description length (MDL) principle has been developed from ideas in
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The process of likelihood-based inference usually involves the following steps:
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should not conclude with the evaluation and summarization of posterior beliefs.
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is studied; this approach quantifies approximation error with, for example, the
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3849:"Model Selection and the Principle of Minimum Description Length: Review paper"
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Statistical Models and Causal Inferences: A Dialogue with the Social Sciences
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Little, Roderick J. (2006). "Calibrated Bayes: A Bayes/Frequentist Roadmap".
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http://upload.wikimedia.org/wikipedia/commons/f/f9/Statistical_Inference.pdf
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Reid, N.; Cox, D. R. (2014). "On Some Principles of Statistical Inference".
4390:
4373:
774:, which has good properties when the data arise from simple random sampling.
666:, i.e. a particular value that best approximates some parameter of interest;
6813:
6746:
6723:
6638:
5968:
5264:
5162:
5097:
5039:
5024:
4961:
4916:
4071:
3759:
3755:
3722:
3525:
3223:
3198:
3194:
3148:
3099:
3064:
Dinov, Ivo; Palanimalai, Selvam; Khare, Ashwini; Christou, Nicolas (2018).
3041:
3006:
Dinov, Ivo; Palanimalai, Selvam; Khare, Ashwini; Christou, Nicolas (2018).
2761:
2173:
2165:
2161:
2160:
developed "structural inference" or "pivotal inference", an approach using
1719:
921:
250:
4497:
4237:
3490:
1623:
6856:
6818:
6501:
6402:
6264:
6077:
6044:
5536:
5453:
5448:
5092:
5049:
5029:
5009:
4999:
4768:
2258:
2097:
1278:
are random and independent with a common conditional distribution, i.e.,
865:
655:
377:
323:
223:
178:
4612:
3505:
3284:
2648:, volume 36 of Encyclopedia of Mathematics. Cambridge University Press.
1964:
1651:. In particular, frequentist developments of optimal inference (such as
745:
Statisticians distinguish between three levels of modeling assumptions;
692:, i.e. a set of values containing, for example, 95% of posterior belief;
5702:
5182:
4882:
4813:
4763:
4738:
4658:
4550:
4399:
4326:
4097:
4036:
3884:
3566:
3470:
3457:
3081:
3023:
2775:
2331:
2247:
1726:
1675:
1563:
Bandyopadhyay & Forster describe four paradigms: The classical (or
431:
335:
303:
275:
193:
183:
173:
4460:
2999:
2055:
for the data, as might be done in frequentist or Bayesian approaches.
805:
5855:
5707:
5327:
5122:
5034:
5019:
5014:
4979:
3232:
3057:
2897:
2002:
989:
856:
measure how close a limiting distribution approaches the statistic's
787:
372:
4027:
4008:
1881:
such as a hierarchical model with multiple levels of random effects.
1029:
For example, model-free simple linear regression is based either on
574:
and deriving estimates. It is assumed that the observed data set is
5371:
4989:
4866:
4861:
4856:
4452:
3684:
3320:
1632:(or classical inference) is that it is applicable only in terms of
298:
96:
91:
46:
3822:
Hampel, Frank R. (February 2003). "The proper fiducial argument".
3557:
658:. Some common forms of statistical proposition are the following:
6876:
6577:
3824:
Seminar fĂŒr Statistik, Eidgenössische Technische Hochschule (ETH)
3155:
2657:
1603:
1146:{\displaystyle (X_{1},Y_{1}),(X_{2},Y_{2}),\cdots ,(X_{n},Y_{n})}
1011:
864:
approximates (to two digits of accuracy) the distribution of the
3395:
Joseph F. Traub, G. W. Wasilkowski, and H. Wozniakowski. (1988)
3106:
2864:
2597:
Combined Survey Sampling Inference: Weighing of Basu's Elephants
6798:
5779:
5753:
5733:
4984:
4775:
4513:
Lindley, D (1958). "Fiducial distribution and Bayes' theorem".
4108:(1877â1878), "Illustrations of the logic of science" (series),
3844:
2660:
Statistical Decision Theory: Estimation, Testing, and Selection
880:
to quantify the error of approximation. In this approach, the
4627:
4238:
Pfanzagl, Johann; with the assistance of R. Hamböker (1994).
3063:
3005:
2785:
2783:
2172:
developed a general theory for structural inference based on
4622:
4349:
2931:
Improving Almost Anything: Ideas and Essays, Revised Edition
2156:
Developing ideas of Fisher and of Pitman from 1938 to 1939,
1933:
based on asymptotic theory or simulation techniques such as
1643:. However, some elements of frequentist statistics, such as
1219:
are deterministic, but the corresponding response variables
4718:
4621:
An online, Bayesian (MCMC) demo/calculator is available at
4575:
Sagitov, Serik (2022). "Statistical Inference". Wikibooks.
3203:
Philosophical Transactions of the Royal Society of London A
2780:
1770:
take place in this decision-theoretic framework, and that
1360:{\displaystyle P\left(Y_{j}\leq y|X_{j}=x\right)=D_{x}(y)}
3696:. Vol. 1 (Second (updated printing 2007) ed.).
1833:, quantifies the probability of observing the given data
1667:
loss functions, in that they minimize expected loss, and
794:
762:
is a widely used and flexible class of parametric models.
3747:(1955), "Statistical methods and scientific induction",
2558:(2008) "Survival analysis: An Epidemiological hazard?".
4276:. Series in Computer Science. Vol. 15. Singapore:
4171:(1878 August), "Deduction, Induction, and Hypothesis",
2136:
and, since this has not invalidated the application of
1624:
Frequentist inference, objectivity, and decision theory
728:
Any statistical inference requires some assumptions. A
2502:
Probability and Statistics: The Science of Uncertainty
608:), and using a model for prediction is referred to as
6953:
3539:
Taraldsen, Gunnar; Lindqvist, Bo Henry (2013-02-01).
3112:
2718:
2698:
2464:"Statistical inference - Encyclopedia of Mathematics"
2184:
The topics below are usually included in the area of
1895:
1859:
1839:
1802:
1528:
1495:
1437:
1397:
1373:
1284:
1225:
1166:
1153:
are independent and identically distributed (iid), or
1043:
1020:
6540:
Autoregressive conditional heteroskedasticity (ARCH)
4217:
3911:
3678:
3344:
2870:
2084:
The MDL principle has been applied in communication-
1727:
Bayesian inference, subjectivity and decision theory
1597:
4074:(1956). "Note on an article by Sir Ronald Fisher".
3754:, 17, 69â78. (criticism of statistical theories of
3679:Bandyopadhyay, P. S.; Forster, M. R., eds. (2011),
3620:. Springer Series in Statistics. pp. 134â174.
2257:parameters and it was the main purpose of studying
2250:of future observations based on past observations.
860:: For example, with 10,000 independent samples the
6002:
4617:National Programme on Technology Enhanced Learning
4515:Journal of the Royal Statistical Society, Series B
4469:Applied Statistical InferenceâLikelihood and Bayes
4222:
4077:Journal of the Royal Statistical Society, Series B
3694:Mathematical statistics: Basic and selected topics
3538:
3425:
2724:
2704:
2124:was an approach to statistical inference based on
1910:
1865:
1845:
1825:
1543:
1510:
1481:
1419:
1379:
1359:
1270:
1211:
1145:
4048:Asymptotic Methods of Statistical Decision Theory
3308:Communications in Statistics - Theory and Methods
3164:"Model-free inference in statistics: how and why"
2658:Liese, Friedrich & Miescke, Klaus-J. (2008).
1704:
6977:
2027:
6088:Multivariate adaptive regression splines (MARS)
4439:(1991). "Statistical models and shoe leather".
4353:; Wasilkowski, G. W.; Wozniakowski, H. (1988).
4306:Journal of the American Statistical Association
4202:, Little, Brown, and Company. (Reprinted 1983,
4014:Journal of the American Statistical Association
3854:Journal of the American Statistical Association
2144:as a special case of an inference theory using
2024:of the model and the simplicity of the model.)
740:
4135:(1878 April), "The Probability of Induction",
1853:, assuming a specific set of parameter values
1012:Model-based analysis of randomized experiments
4643:
4485:British Journal for the Philosophy of Science
3998:Information Criteria and Statistical Modeling
2426:. Springer: The European Mathematical Society
2253:Initially, predictive inference was based on
2032:
1777:
946:
529:
4274:Stochastic Complexity in Statistical Inquiry
3691:
2902:Statistics and Truth: Putting Chance to Work
830:
654:of a statistical inference is a statistical
4114:, vols. 12â13. Relevant individual papers:
3692:Bickel, Peter J.; Doksum, Kjell A. (2001).
3054:Hinkelmann and Kempthorne (2008) Chapter 6.
4688:
4650:
4636:
4194:(1883), "A Theory of probable inference",
4065:Introduction to the Practice of Statistics
3959:
3936:
3615:
3595:
2624:Probability With a View Towards Statistics
2590:
2588:
2485:
2483:
1554:
972:, randomization is also of importance: in
868:for many population distributions, by the
536:
522:
53:
5301:
4558:
4389:
4117:(1878 March), "The Doctrine of Chances",
4026:
3979:
3866:
3556:
3319:
3222:
3138:
3089:
3031:
1271:{\displaystyle Y_{1},Y_{2},\cdots ,Y_{n}}
1212:{\displaystyle X_{1},X_{2},\cdots ,X_{n}}
912:results are often invoked to justify the
713:
4435:
4268:
3842:
3765:
3750:Journal of the Royal Statistical Society
3370:
3368:
2767:Journal of the Royal Statistical Society
1582:
804:
790:is a set of semi-parametric assumptions.
467:Library and information science software
4536:
4512:
4474:
4003:
3940:(1963). "On tables of random numbers".
3771:Statistical Models: Theory and practice
3642:
3541:"Fiducial theory and optimal inference"
3161:
2618:
2616:
2585:
2480:
2417:
2241:
2151:
822:regression-based inference. The use of
14:
6978:
6614:KaplanâMeier estimator (product limit)
4371:
4070:
4063:; McCabe, G. P.; Craig, B. A. (2015),
3821:
3503:
3468:
3431:
3254:
3193:
2920:Peirce; Freedman; Moore et al. (2015).
2594:
1949:
1929:, confidence intervals, or conducting
795:Importance of valid models/assumptions
462:Geographic information system software
6687:
6254:
6001:
5300:
5070:
4687:
4631:
4302:
3654:Predictive Inference: An Introduction
3382:
3380:
3365:
3358:
3356:
2646:Comparison of Statistical Experiments
2498:
2444:Konishi & Kitagawa (2008), p. 75.
2369:
2110:
1685:
558:to infer properties of an underlying
6924:
6624:Accelerated failure time (AFT) model
4374:"R. A. Fisher and Fiducial Argument"
4153:(1878 June), "The Order of Nature",
3598:Annales de l'Institut Henri Poincaré
2613:
2505:. Freeman and Company. p. 267.
2499:Evans, Michael; et al. (2004).
1959:
1653:minimum-variance unbiased estimators
1367:, which is independent of the index
6936:
6219:Analysis of variance (ANOVA, anova)
5071:
4583:Essentials of Statistical Inference
4221:; Pisani, R.; Purves, R.A. (1978).
3919:Introduction to Experimental Design
3727:Principles of Statistical Inference
3305:
2996:Hinkelmann & Kempthorne (2008)
2179:
1941:analysis, or graphical diagnostics.
1885:different values of the parameters.
1792:approaches statistics by using the
24:
6314:CochranâMantelâHaenszel statistics
4940:Pearson product-moment correlation
4527:10.1111/j.2517-6161.1958.tb00278.x
4408:
4090:10.1111/j.2517-6161.1956.tb00236.x
3377:
3353:
2871:Freedman, Pisani & Purves 1978
2418:Johnson, Richard (12 March 2016).
1037:, where the pairs of observations
1021:Model-free randomization inference
25:
7027:
4596:
4581:Young, G.A., Smith, R.L. (2005).
4204:John Benjamins Publishing Company
3996:Konishi S., Kitagawa G. (2008),
2692:"Indeed, limit theorems 'as
1701:for using the Bayesian approach.
1598:Examples of frequentist inference
903:With indefinitely large samples,
6963:
6935:
6923:
6911:
6898:
6897:
6688:
4539:International Statistical Review
2789:Hinkelmann and Kempthorne(2008)
1963:
1699:several different justifications
1482:{\displaystyle \mu (x)=E(Y|X=x)}
918:generalized estimating equations
564:Inferential statistical analysis
6573:Least-squares spectral analysis
3589:
3532:
3497:
3462:
3446:
3437:
3416:
3407:
3398:
3389:
3374:Hansen and Yu (2001), page 747.
3299:
3248:
3239:
3187:
3178:
3048:
2990:
2973:
2952:
2943:
2929:Box, G.E.P. and Friends (2006)
2923:
2914:
2891:
2876:
2855:
2846:
2826:
2817:
2801:
2792:
2755:
2745:
2735:
2686:
2676:
2651:
2638:
2629:
2565:
2549:
2540:
2519:
2377:Oxford Dictionary of Statistics
2213:Revising opinions in statistics
2105:computational complexity theory
2072:maximum a posteriori estimation
1657:uniformly most powerful testing
623:
5554:Mean-unbiased minimum-variance
4657:
4319:10.1080/01621459.2000.10474346
3960:Kolmogorov, Andrei N. (1998).
2492:
2456:
2447:
2438:
2411:
2390:
2344:
2297:Informal inferential reasoning
2208:Statistical hypothesis testing
1902:
1820:
1813:
1806:
1741:but not yet fully developed.)
1705:Examples of Bayesian inference
1577:Akaikean-Information Criterion
1538:
1532:
1505:
1499:
1476:
1463:
1456:
1447:
1441:
1414:
1408:
1354:
1348:
1310:
1140:
1114:
1102:
1076:
1070:
1044:
841:Asymptotic theory (statistics)
548:Process of using data analysis
13:
1:
6867:Geographic information system
6083:Simultaneous equations models
4240:Parametric Statistical Theory
3981:10.1016/S0304-3975(98)00075-9
3962:"On tables of random numbers"
3330:10.1080/03610926.2021.1921214
2357:
2146:upper and lower probabilities
2068:maximum likelihood estimation
2028:Other paradigms for inference
914:generalized method of moments
6050:Coefficient of determination
5661:Uniformly most powerful test
4355:Information-Based Complexity
4067:, Eighth Edition, Macmillan.
3967:Theoretical Computer Science
3626:10.1007/978-1-4612-0919-5_10
2599:. Hodder Arnold. p. 6.
2529:Cambridge University Press.
2362:
2236:Summarizing statistical data
1998:Akaike information criterion
1956:Akaike information criterion
1920:maximum likelihood estimates
1826:{\displaystyle L(x|\theta )}
978:sampling without replacement
939:(such as with one-parameter
801:Statistical model validation
772:HodgesâLehmannâSen estimator
741:Degree of models/assumptions
7:
6619:Proportional hazards models
6563:Spectral density estimation
6545:Vector autoregression (VAR)
5979:Maximum posterior estimator
5211:Randomized controlled trial
4467:Held L., Bové D.S. (2014).
3618:Breakthroughs in Statistics
3507:Inference and linear models
2961:David A. Freedman et alias
2683:a finite number of trials".
2622:Jörgen Hoffman-Jörgensen's
2525:van der Vaart, A.W. (1998)
2424:Encyclopedia of Mathematics
2398:"TensorFlow Lite inference"
2375:Upton, G., Cook, I. (2008)
2280:
2198:Statistical decision theory
2005:of the relative quality of
1645:statistical decision theory
890:KullbackâLeibler divergence
709:of data points into groups.
560:distribution of probability
10:
7032:
6379:Multivariate distributions
4799:Average absolute deviation
4475:Lenhard, Johannes (2006).
4372:Zabell, S. L. (Aug 1992).
4231:W. W. Norton & Company
4121:, v. 12, March issue, pp.
3922:(Second ed.). Wiley.
3877:10.1198/016214501753168398
3816:Cambridge University Press
3775:Cambridge University Press
3731:Cambridge University Press
3671:
3472:The structure of inference
2468:www.encyclopediaofmath.org
2114:
2039:Minimum description length
2036:
2033:Minimum description length
1953:
1911:{\displaystyle {\bar {y}}}
1781:
1778:Likelihood-based inference
1689:
1586:
956:
950:
947:Randomization-based models
834:
798:
717:
578:from a larger population.
26:
6893:
6847:
6784:
6737:
6700:
6696:
6683:
6655:
6637:
6604:
6595:
6553:
6500:
6461:
6410:
6401:
6367:Structural equation model
6322:
6279:
6275:
6250:
6209:
6175:
6129:
6096:
6058:
6025:
6021:
5997:
5937:
5846:
5765:
5729:
5720:
5703:Score/Lagrange multiplier
5688:
5641:
5586:
5512:
5503:
5313:
5309:
5296:
5255:
5229:
5181:
5136:
5118:Sample size determination
5083:
5079:
5066:
4970:
4925:
4899:
4881:
4837:
4789:
4709:
4700:
4696:
4683:
4665:
3504:Fraser, D. A. S. (1979).
3469:Fraser, D. A. S. (1968).
3257:The American Statistician
3131:10.1007/s12021-018-9406-9
2560:The American Statistician
886:probability distributions
831:Approximate distributions
817:Incorrect assumptions of
760:generalized linear models
489:Qualitative data analysis
6862:Environmental statistics
6384:Elliptical distributions
6177:Generalized linear model
6106:Simple linear regression
5876:HodgesâLehmann estimator
5333:Probability distribution
5242:Stochastic approximation
4804:Coefficient of variation
4441:Sociological Methodology
3836:10.3929/ethz-a-004526011
3814:, and Philip B. Stark),
3681:Philosophy of Statistics
3545:The Annals of Statistics
3386:Rissanen (1989), page 84
3269:10.1198/000313006X117837
2949:Cox (2006), p. 196.
2337:
2312:Philosophy of statistics
2302:Information field theory
2269:). De Finetti's idea of
2134:probability distribution
1420:{\displaystyle D_{x}(.)}
819:'simple' random sampling
756:'simple' random sampling
554:is the process of using
29:Statistical interference
27:Not to be confused with
6522:Cross-correlation (XCF)
6130:Non-standard predictors
5564:LehmannâScheffĂ© theorem
5237:Adaptive clinical trial
4173:Popular Science Monthly
4155:Popular Science Monthly
4137:Popular Science Monthly
4119:Popular Science Monthly
4111:Popular Science Monthly
3777:. pp. xiv+442 pp.
3510:. London: McGraw-Hill.
2983:5 (4): 465â472. Trans.
2420:"Statistical Inference"
2193:Statistical assumptions
2162:invariant probabilities
2130:confidence distribution
1866:{\displaystyle \theta }
1555:Paradigms for inference
1544:{\displaystyle \mu (x)}
1511:{\displaystyle \mu (x)}
920:, which are popular in
724:Statistical assumptions
584:can be contrasted with
566:infers properties of a
6918:Mathematics portal
6739:Engineering statistics
6647:NelsonâAalen estimator
6224:Analysis of covariance
6111:Ordinary least squares
6035:Pearson product-moment
5439:Statistical functional
5350:Empirical distribution
5183:Controlled experiments
4912:Frequency distribution
4690:Descriptive statistics
4102:(reply to Fisher 1955)
3362:Hansen & Yu (2001)
3224:10.1098/rsta.1937.0005
3162:Politis, D.N. (2019).
2837:Bayesian Data Analysis
2726:
2706:
2644:Erik Torgerson (1991)
2292:Induction (philosophy)
1912:
1867:
1847:
1827:
1766:assert that inference
1628:One interpretation of
1545:
1512:
1483:
1421:
1381:
1361:
1272:
1213:
1160:, where the variables
1147:
998:randomized experiments
810:
735:Descriptive statistics
714:Models and assumptions
586:descriptive statistics
582:Inferential statistics
368:Inferential statistics
314:Descriptive statistics
261:Human subject research
7006:Philosophy of science
6986:Statistical inference
6834:Population statistics
6776:System identification
6510:Autocorrelation (ACF)
6438:Exponential smoothing
6352:Discriminant analysis
6347:Canonical correlation
6211:Partition of variance
6073:Regression validation
5917:(JonckheereâTerpstra)
5816:Likelihood-ratio test
5505:Frequentist inference
5417:Locationâscale family
5338:Sampling distribution
5303:Statistical inference
5270:Cross-sectional study
5257:Observational studies
5216:Randomized experiment
5045:Stem-and-leaf display
4847:Central limit theorem
4613:Statistical Inference
4603:Statistical Inference
4423:Statistical Inference
4391:10.1214/ss/1177011233
3938:Kolmogorov, Andrei N.
2987:and Terence P. Speed.
2727:
2707:
2626:, Volume I. Page 399
2527:Asymptotic Statistics
2307:Population proportion
2287:Algorithmic inference
2242:Predictive inference
2218:Design of experiments
2186:statistical inference
2064:source coding theorem
2049:Kolmogorov complexity
1913:
1868:
1848:
1828:
1634:frequency probability
1630:frequentist inference
1589:Frequentist inference
1583:Frequentist inference
1546:
1513:
1484:
1422:
1382:
1362:
1273:
1214:
1148:
909:central limit theorem
854:approximation results
852:With finite samples,
808:
552:Statistical inference
152:Philosophical schools
7001:Logic and statistics
6757:Probabilistic design
6342:Principal components
6185:Exponential families
6137:Nonlinear regression
6116:General linear model
6078:Mixed effects models
6068:Errors and residuals
6045:Confounding variable
5947:Bayesian probability
5925:Van der Waerden test
5915:Ordered alternative
5680:Multiple comparisons
5559:RaoâBlackwellization
5522:Estimating equations
5478:Statistical distance
5196:Factorial experiment
4729:Arithmetic-Geometric
3773:(revised ed.).
3245:Preface to Pfanzagl.
2968:Moore et al. (2015).
2904:, World Scientific.
2823:Moore et al. (2015).
2716:
2696:
2595:Brewer, Ken (2002).
2327:Predictive modelling
2322:Predictive analytics
2222:analysis of variance
2152:Structural inference
2138:confidence intervals
2126:fiducial probability
1893:
1857:
1837:
1800:
1762:. Some advocates of
1722:for model comparison
1619:significance testing
1526:
1493:
1435:
1395:
1371:
1282:
1223:
1164:
1158:deterministic design
1041:
941:exponential families
874:approximation theory
870:BerryâEsseen theorem
845:Approximation theory
837:Statistical distance
618:predictive inference
477:Reference management
427:Scientific modelling
169:Critical rationalism
6996:Deductive reasoning
6991:Inductive reasoning
6829:Official statistics
6752:Methods engineering
6433:Seasonal adjustment
6201:Poisson regressions
6121:Bayesian regression
6060:Regression analysis
6040:Partial correlation
6012:Regression analysis
5611:Prediction interval
5606:Likelihood interval
5596:Confidence interval
5588:Interval estimation
5549:Unbiased estimators
5367:Model specification
5247:Up-and-down designs
4935:Partial correlation
4891:Index of dispersion
4809:Interquartile range
4498:10.1093/bjps/axi152
4378:Statistical Science
3912:Hinkelmann, Klaus;
3475:. New York: Wiley.
3215:1937RSPTA.236..333N
3070:Teaching Statistics
3012:Teaching Statistics
2985:Dorota M. Dabrowska
2981:Statistical Science
2575:Sage Publications.
2453:Cox (2006), p. 197.
2317:Prediction interval
2267:celestial mechanics
2170:Donald A. S. Fraser
1950:AIC-based inference
1794:likelihood function
1715:interval estimation
1612:Confidence interval
878:functional analysis
862:normal distribution
858:sample distribution
675:confidence interval
457:Argument technology
18:Sampling statistics
6849:Spatial statistics
6729:Medical statistics
6629:First hitting time
6583:Whittle likelihood
6234:Degrees of freedom
6229:Multivariate ANOVA
6162:Heteroscedasticity
5974:Bayesian estimator
5939:Bayesian inference
5788:KolmogorovâSmirnov
5673:Randomization test
5643:Testing hypotheses
5616:Tolerance interval
5527:Maximum likelihood
5422:Exponential family
5355:Density estimation
5315:Statistical theory
5275:Natural experiment
5221:Scientific control
5138:Survey methodology
4824:Standard deviation
4607:MIT OpenCourseWare
4560:10.1111/insr.12067
4551:10.1111/insr.12067
4357:. Academic Press.
4313:(452): 1349â1353.
3567:10.1214/13-AOS1083
3443:Davison, page 12.
3422:Cox (2006) page 66
3082:10.1111/test.12156
3024:10.1111/test.12156
2886:Statistical Models
2852:Peirce (1877-1878)
2841:Chapman & Hall
2770:, 97 (4), 557â625
2722:
2702:
2122:Fiducial inference
2117:Fiducial inference
2111:Fiducial inference
2090:information theory
2053:probability models
2047:and the theory of
2045:information theory
2018:information theory
2016:AIC is founded on
2007:statistical models
1975:. You can help by
1908:
1863:
1843:
1823:
1772:Bayesian inference
1764:Bayesian inference
1751:decision theoretic
1692:Bayesian inference
1686:Bayesian inference
1575:paradigm, and the
1541:
1508:
1479:
1417:
1377:
1357:
1268:
1209:
1143:
970:Bayesian inference
898:Hellinger distance
894:Bregman divergence
811:
784:heteroscedasticity
572:testing hypotheses
451:Tools and software
395:Secondary research
319:Discourse analysis
7011:Scientific method
6951:
6950:
6889:
6888:
6885:
6884:
6824:National accounts
6794:Actuarial science
6786:Social statistics
6679:
6678:
6675:
6674:
6671:
6670:
6606:Survival function
6591:
6590:
6453:Granger causality
6294:Contingency table
6269:Survival analysis
6246:
6245:
6242:
6241:
6098:Linear regression
5993:
5992:
5989:
5988:
5964:Credible interval
5933:
5932:
5716:
5715:
5532:Method of moments
5401:Parametric family
5362:Statistical model
5292:
5291:
5288:
5287:
5206:Random assignment
5128:Statistical power
5062:
5061:
5058:
5057:
4907:Contingency table
4877:
4876:
4744:Generalized/power
4615:â lecture by the
4605:â lecture on the
4425:. Duxbury Press.
4364:978-0-12-697545-1
4287:978-9971-5-0859-3
4253:978-3-11-013863-4
4244:Walter de Gruyter
4007:(December 1988).
3929:978-0-471-72756-9
3914:Kempthorne, Oscar
3843:Hansen, Mark H.;
3784:978-0-521-74385-3
3707:978-0-13-850363-5
3635:978-0-387-94037-3
2939:978-0-471-72755-2
2883:David A. Freedman
2808:David A. Freedman
2725:{\displaystyle n}
2705:{\displaystyle n}
2669:978-0-387-73193-3
2489:Cox (2006) page 2
2385:978-0-19-954145-4
2203:Estimation theory
2158:George A. Barnard
2142:fiducial argument
2094:linear regression
1993:
1992:
1905:
1846:{\displaystyle x}
1747:optimal decisions
1711:Credible interval
1649:utility functions
1647:, do incorporate
1641:utility functions
1579:-based paradigm.
1380:{\displaystyle j}
986:missing at random
963:Random assignment
730:statistical model
720:Statistical model
690:credible interval
671:interval estimate
638:statistical model
570:, for example by
546:
545:
512:Philosophy portal
420:Systematic review
405:Literature review
363:Historical method
346:Social experiment
281:Scientific method
266:Narrative inquiry
117:Interdisciplinary
111:Research strategy
16:(Redirected from
7023:
6968:
6967:
6959:
6939:
6938:
6927:
6926:
6916:
6915:
6901:
6900:
6804:Crime statistics
6698:
6697:
6685:
6684:
6602:
6601:
6568:Fourier analysis
6555:Frequency domain
6535:
6482:
6448:Structural break
6408:
6407:
6357:Cluster analysis
6304:Log-linear model
6277:
6276:
6252:
6251:
6193:
6167:Homoscedasticity
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6022:
5999:
5998:
5918:
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5902:
5901:(KruskalâWallis)
5886:
5871:
5826:Cross validation
5811:
5793:AndersonâDarling
5740:
5727:
5726:
5698:Likelihood-ratio
5690:Parametric tests
5668:Permutation test
5651:1- & 2-tails
5542:Minimum distance
5514:Point estimation
5510:
5509:
5461:Optimal decision
5412:
5311:
5310:
5298:
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5280:Quasi-experiment
5230:Adaptive designs
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4945:Rank correlation
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4351:Traub, Joseph F.
4346:
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4278:World Scientific
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4196:Studies in Logic
4181:Internet Archive
4163:Internet Archive
4145:Internet Archive
4127:Internet Archive
4101:
4040:
4030:
4021:(404): 929â940.
4005:Kruskal, William
3993:
3983:
3957:
3933:
3908:
3903:. Archived from
3870:
3861:(454): 746â774.
3839:
3796:
3719:
3687:
3665:
3649:Geisser, Seymour
3646:
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2263:Bruno de Finetti
2180:Inference topics
1988:
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1734:utility function
1567:) paradigm, the
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1520:conditional mean
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1429:conditional mean
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905:limiting results
758:. The family of
751:Fully parametric
683:confidence level
590:machine learning
538:
531:
524:
484:Science software
383:Cultural mapping
351:Quasi-experiment
341:Field experiment
309:Content analysis
204:Critical realism
122:Multimethodology
57:
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6766:quality control
6733:
6715:Clinical trials
6692:
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6639:Hazard function
6633:
6587:
6549:
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6496:
6492:BreuschâGodfrey
6480:
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6397:
6372:Factor analysis
6318:
6299:Graphical model
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5848:Rank statistics
5842:
5821:Model selection
5809:
5767:Goodness of fit
5761:
5738:
5712:
5684:
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5582:
5571:Median unbiased
5499:
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5343:Order statistic
5305:
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5177:
5132:
5075:
5073:Data collection
5054:
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4702:Continuous data
4692:
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4599:
4479:
4411:
4409:Further reading
4406:
4365:
4288:
4270:Rissanen, Jorma
4254:
4028:10.2307/2290117
3930:
3800:Freedman, D. A.
3785:
3767:Freedman, D. A.
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2847:
2835:et al. (2013).
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2275:Seymour Geisser
2271:exchangeability
2244:
2231:Survey sampling
2182:
2154:
2132:is not a valid
2119:
2113:
2079:Bayesian priors
2076:maximum-entropy
2041:
2035:
2030:
2022:goodness of fit
2011:model selection
1989:
1983:
1980:
1973:needs expansion
1958:
1952:
1927:standard errors
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1617:Null hypothesis
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988:assumption for
982:exchangeability
974:survey sampling
965:
955:
949:
916:and the use of
882:metric geometry
847:
835:Main articles:
833:
803:
797:
779:Semi-parametric
743:
726:
718:Main articles:
716:
695:rejection of a
626:
549:
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444:
443:
390:Phenomenography
329:Autoethnography
294:
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246:Grounded theory
241:Critical theory
236:Art methodology
231:Action research
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65:Research design
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6710:Bioinformatics
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6515:partial (PACF)
6506:
6504:
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6462:Specific tests
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6362:Classification
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6309:McNemar's test
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6147:Semiparametric
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5777:
5771:
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5759:
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5722:Specific tests
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5293:
5290:
5289:
5286:
5285:
5283:
5282:
5277:
5272:
5267:
5261:
5259:
5253:
5252:
5250:
5249:
5244:
5239:
5233:
5231:
5227:
5226:
5224:
5223:
5218:
5213:
5208:
5203:
5198:
5193:
5187:
5185:
5179:
5178:
5176:
5175:
5173:Standard error
5170:
5165:
5160:
5159:
5158:
5153:
5142:
5140:
5134:
5133:
5131:
5130:
5125:
5120:
5115:
5110:
5105:
5103:Optimal design
5100:
5095:
5089:
5087:
5077:
5076:
5064:
5063:
5060:
5059:
5056:
5055:
5053:
5052:
5047:
5042:
5037:
5032:
5027:
5022:
5017:
5012:
5007:
5002:
4997:
4992:
4987:
4982:
4976:
4974:
4968:
4967:
4965:
4964:
4959:
4958:
4957:
4952:
4942:
4937:
4931:
4929:
4923:
4922:
4920:
4919:
4914:
4909:
4903:
4901:
4900:Summary tables
4897:
4896:
4894:
4893:
4887:
4885:
4879:
4878:
4875:
4874:
4872:
4871:
4870:
4869:
4864:
4859:
4849:
4843:
4841:
4835:
4834:
4832:
4831:
4826:
4821:
4816:
4811:
4806:
4801:
4795:
4793:
4787:
4786:
4784:
4783:
4778:
4773:
4772:
4771:
4766:
4761:
4756:
4751:
4746:
4741:
4736:
4734:Contraharmonic
4731:
4726:
4715:
4713:
4704:
4694:
4693:
4681:
4680:
4678:
4677:
4672:
4666:
4663:
4662:
4655:
4654:
4647:
4640:
4632:
4626:
4625:
4619:
4610:
4598:
4597:External links
4595:
4594:
4593:
4579:
4573:
4545:(2): 293â308.
4534:
4531:
4510:
4472:
4465:
4453:10.2307/270939
4437:Freedman, D.A.
4433:
4410:
4407:
4405:
4404:
4384:(3): 369â387.
4369:
4363:
4347:
4300:
4286:
4266:
4252:
4235:
4215:
4189:
4188:
4187:
4169:
4151:
4133:
4103:
4084:(2): 288â294.
4068:
4058:
4044:Le Cam, Lucian
4041:
4001:
3994:
3974:(2): 387â395.
3942:SankhyÄ Ser. A
3934:
3928:
3909:
3907:on 2004-11-16.
3868:10.1.1.43.6581
3840:
3819:
3812:Jasjeet Sekhon
3797:
3783:
3763:
3742:
3720:
3706:
3689:
3675:
3673:
3670:
3667:
3666:
3641:
3634:
3614:Translated in
3588:
3531:
3516:
3496:
3481:
3461:
3445:
3436:
3424:
3415:
3406:
3397:
3388:
3376:
3364:
3352:
3343:
3298:
3263:(3): 213â223.
3247:
3238:
3186:
3177:
3154:
3125:(3): 407â421.
3105:
3056:
3047:
2998:
2989:
2972:
2970:
2969:
2966:
2951:
2942:
2922:
2913:
2890:
2875:
2863:
2854:
2845:
2825:
2816:
2800:
2791:
2779:
2754:
2744:
2734:
2721:
2701:
2685:
2675:
2668:
2650:
2637:
2635:Le Cam (1986)
2628:
2612:
2606:978-0340692295
2605:
2584:
2564:
2556:Freedman, D.A.
2548:
2539:
2518:
2511:
2491:
2479:
2455:
2446:
2437:
2410:
2389:
2367:
2366:
2364:
2361:
2359:
2356:
2353:
2352:
2342:
2341:
2339:
2336:
2335:
2334:
2329:
2324:
2319:
2314:
2309:
2304:
2299:
2294:
2289:
2282:
2279:
2243:
2240:
2239:
2238:
2233:
2228:
2215:
2210:
2205:
2200:
2195:
2181:
2178:
2166:group families
2153:
2150:
2115:Main article:
2112:
2109:
2037:Main article:
2034:
2031:
2029:
2026:
1991:
1990:
1970:
1968:
1954:Main article:
1951:
1948:
1947:
1946:
1942:
1938:
1923:
1904:
1901:
1886:
1882:
1862:
1842:
1822:
1819:
1815:
1811:
1808:
1805:
1782:Main article:
1779:
1776:
1728:
1725:
1724:
1723:
1717:
1706:
1703:
1687:
1684:
1680:test statistic
1665:absolute value
1661:loss functions
1659:) make use of
1625:
1622:
1621:
1620:
1614:
1609:
1599:
1596:
1587:Main article:
1584:
1581:
1571:paradigm, the
1556:
1553:
1540:
1537:
1534:
1531:
1507:
1504:
1501:
1498:
1478:
1475:
1472:
1469:
1465:
1461:
1458:
1455:
1452:
1449:
1446:
1443:
1440:
1416:
1413:
1410:
1405:
1401:
1389:
1388:
1376:
1356:
1353:
1350:
1345:
1341:
1337:
1333:
1329:
1326:
1321:
1317:
1312:
1308:
1305:
1300:
1296:
1291:
1287:
1265:
1261:
1257:
1254:
1251:
1246:
1242:
1238:
1233:
1229:
1206:
1202:
1198:
1195:
1192:
1187:
1183:
1179:
1174:
1170:
1154:
1142:
1137:
1133:
1129:
1124:
1120:
1116:
1113:
1110:
1107:
1104:
1099:
1095:
1091:
1086:
1082:
1078:
1075:
1072:
1067:
1063:
1059:
1054:
1050:
1046:
1026:observations.
1022:
1019:
1013:
1010:
951:Main article:
948:
945:
832:
829:
796:
793:
792:
791:
775:
767:Non-parametric
763:
742:
739:
715:
712:
711:
710:
707:classification
700:
693:
686:
681:at the stated
667:
664:point estimate
625:
622:
547:
544:
543:
541:
540:
533:
526:
518:
515:
514:
508:
507:
504:
503:
502:
501:
496:
491:
481:
480:
479:
474:
464:
459:
453:
450:
449:
446:
445:
442:
441:
436:
435:
434:
424:
423:
422:
417:
415:Scoping review
412:
407:
402:
392:
387:
386:
385:
375:
370:
365:
360:
358:Field research
355:
354:
353:
348:
343:
333:
332:
331:
321:
316:
311:
306:
301:
295:
292:
291:
288:
287:
284:
283:
278:
273:
268:
263:
258:
256:Historiography
253:
248:
243:
238:
233:
227:
222:
221:
218:
217:
214:
213:
212:
211:
209:Subtle realism
206:
196:
191:
189:Postpositivism
186:
181:
176:
171:
166:
164:Constructivism
161:
159:Antipositivism
155:
150:
149:
146:
145:
142:
141:
136:
135:
134:
124:
119:
113:
110:
109:
106:
105:
102:
101:
100:
99:
94:
84:
79:
74:
68:
63:
62:
59:
58:
50:
49:
43:
42:
9:
6:
4:
3:
2:
7028:
7017:
7016:Psychometrics
7014:
7012:
7009:
7007:
7004:
7002:
6999:
6997:
6994:
6992:
6989:
6987:
6984:
6983:
6981:
6971:
6966:
6961:
6960:
6957:
6944:
6943:
6934:
6932:
6931:
6922:
6920:
6919:
6914:
6908:
6906:
6905:
6896:
6895:
6892:
6878:
6875:
6873:
6872:Geostatistics
6870:
6868:
6865:
6863:
6860:
6858:
6855:
6854:
6852:
6850:
6846:
6840:
6839:Psychometrics
6837:
6835:
6832:
6830:
6827:
6825:
6822:
6820:
6817:
6815:
6812:
6810:
6807:
6805:
6802:
6800:
6797:
6795:
6792:
6791:
6789:
6787:
6783:
6777:
6774:
6772:
6769:
6767:
6763:
6760:
6758:
6755:
6753:
6750:
6748:
6745:
6744:
6742:
6740:
6736:
6730:
6727:
6725:
6722:
6720:
6716:
6713:
6711:
6708:
6707:
6705:
6703:
6702:Biostatistics
6699:
6695:
6691:
6686:
6682:
6664:
6663:Log-rank test
6661:
6660:
6658:
6654:
6648:
6645:
6644:
6642:
6640:
6636:
6630:
6627:
6625:
6622:
6620:
6617:
6615:
6612:
6611:
6609:
6607:
6603:
6600:
6598:
6594:
6584:
6581:
6579:
6576:
6574:
6571:
6569:
6566:
6564:
6561:
6560:
6558:
6556:
6552:
6546:
6543:
6541:
6538:
6536:
6534:(BoxâJenkins)
6530:
6528:
6525:
6523:
6520:
6516:
6513:
6512:
6511:
6508:
6507:
6505:
6503:
6499:
6493:
6490:
6488:
6487:DurbinâWatson
6485:
6483:
6477:
6475:
6472:
6470:
6469:DickeyâFuller
6467:
6466:
6464:
6460:
6454:
6451:
6449:
6446:
6444:
6443:Cointegration
6441:
6439:
6436:
6434:
6431:
6429:
6426:
6424:
6421:
6419:
6418:Decomposition
6416:
6415:
6413:
6409:
6406:
6404:
6400:
6390:
6387:
6386:
6385:
6382:
6381:
6380:
6377:
6373:
6370:
6369:
6368:
6365:
6363:
6360:
6358:
6355:
6353:
6350:
6348:
6345:
6343:
6340:
6338:
6335:
6333:
6330:
6329:
6327:
6325:
6321:
6315:
6312:
6310:
6307:
6305:
6302:
6300:
6297:
6295:
6292:
6290:
6289:Cohen's kappa
6287:
6286:
6284:
6282:
6278:
6274:
6270:
6266:
6262:
6258:
6253:
6249:
6235:
6232:
6230:
6227:
6225:
6222:
6220:
6217:
6216:
6214:
6212:
6208:
6202:
6198:
6194:
6188:
6186:
6183:
6182:
6180:
6178:
6174:
6168:
6165:
6163:
6160:
6158:
6155:
6153:
6150:
6148:
6145:
6143:
6142:Nonparametric
6140:
6138:
6135:
6134:
6132:
6128:
6122:
6119:
6117:
6114:
6112:
6109:
6107:
6104:
6103:
6101:
6099:
6095:
6089:
6086:
6084:
6081:
6079:
6076:
6074:
6071:
6069:
6066:
6065:
6063:
6061:
6057:
6051:
6048:
6046:
6043:
6041:
6038:
6036:
6033:
6032:
6030:
6028:
6024:
6020:
6013:
6010:
6008:
6005:
6004:
6000:
5996:
5980:
5977:
5976:
5975:
5972:
5970:
5967:
5965:
5962:
5958:
5955:
5953:
5950:
5949:
5948:
5945:
5944:
5942:
5940:
5936:
5926:
5923:
5919:
5913:
5911:
5905:
5903:
5897:
5896:
5895:
5892:
5891:Nonparametric
5889:
5887:
5881:
5877:
5874:
5873:
5872:
5866:
5862:
5861:Sample median
5859:
5858:
5857:
5854:
5853:
5851:
5849:
5845:
5837:
5834:
5832:
5829:
5827:
5824:
5823:
5822:
5819:
5817:
5814:
5812:
5806:
5804:
5801:
5799:
5796:
5794:
5791:
5789:
5786:
5784:
5782:
5778:
5776:
5773:
5772:
5770:
5768:
5764:
5758:
5756:
5752:
5750:
5748:
5743:
5741:
5736:
5732:
5731:
5728:
5725:
5723:
5719:
5709:
5706:
5704:
5701:
5699:
5696:
5695:
5693:
5691:
5687:
5681:
5678:
5674:
5671:
5670:
5669:
5666:
5662:
5659:
5658:
5657:
5654:
5652:
5649:
5648:
5646:
5644:
5640:
5632:
5629:
5627:
5624:
5623:
5622:
5619:
5617:
5614:
5612:
5609:
5607:
5604:
5602:
5599:
5597:
5594:
5593:
5591:
5589:
5585:
5579:
5576:
5572:
5569:
5565:
5562:
5560:
5557:
5556:
5555:
5552:
5551:
5550:
5547:
5543:
5540:
5538:
5535:
5533:
5530:
5528:
5525:
5524:
5523:
5520:
5519:
5517:
5515:
5511:
5508:
5506:
5502:
5496:
5493:
5491:
5488:
5484:
5481:
5480:
5479:
5476:
5474:
5471:
5467:
5466:loss function
5464:
5463:
5462:
5459:
5455:
5452:
5450:
5447:
5445:
5442:
5441:
5440:
5437:
5435:
5432:
5430:
5427:
5423:
5420:
5418:
5415:
5413:
5407:
5404:
5403:
5402:
5399:
5395:
5392:
5390:
5387:
5385:
5382:
5381:
5380:
5377:
5373:
5370:
5368:
5365:
5364:
5363:
5360:
5356:
5353:
5352:
5351:
5348:
5344:
5341:
5340:
5339:
5336:
5334:
5331:
5329:
5326:
5324:
5321:
5320:
5318:
5316:
5312:
5308:
5304:
5299:
5295:
5281:
5278:
5276:
5273:
5271:
5268:
5266:
5263:
5262:
5260:
5258:
5254:
5248:
5245:
5243:
5240:
5238:
5235:
5234:
5232:
5228:
5222:
5219:
5217:
5214:
5212:
5209:
5207:
5204:
5202:
5199:
5197:
5194:
5192:
5189:
5188:
5186:
5184:
5180:
5174:
5171:
5169:
5168:Questionnaire
5166:
5164:
5161:
5157:
5154:
5152:
5149:
5148:
5147:
5144:
5143:
5141:
5139:
5135:
5129:
5126:
5124:
5121:
5119:
5116:
5114:
5111:
5109:
5106:
5104:
5101:
5099:
5096:
5094:
5091:
5090:
5088:
5086:
5082:
5078:
5074:
5069:
5065:
5051:
5048:
5046:
5043:
5041:
5038:
5036:
5033:
5031:
5028:
5026:
5023:
5021:
5018:
5016:
5013:
5011:
5008:
5006:
5003:
5001:
4998:
4996:
4995:Control chart
4993:
4991:
4988:
4986:
4983:
4981:
4978:
4977:
4975:
4973:
4969:
4963:
4960:
4956:
4953:
4951:
4948:
4947:
4946:
4943:
4941:
4938:
4936:
4933:
4932:
4930:
4928:
4924:
4918:
4915:
4913:
4910:
4908:
4905:
4904:
4902:
4898:
4892:
4889:
4888:
4886:
4884:
4880:
4868:
4865:
4863:
4860:
4858:
4855:
4854:
4853:
4850:
4848:
4845:
4844:
4842:
4840:
4836:
4830:
4827:
4825:
4822:
4820:
4817:
4815:
4812:
4810:
4807:
4805:
4802:
4800:
4797:
4796:
4794:
4792:
4788:
4782:
4779:
4777:
4774:
4770:
4767:
4765:
4762:
4760:
4757:
4755:
4752:
4750:
4747:
4745:
4742:
4740:
4737:
4735:
4732:
4730:
4727:
4725:
4722:
4721:
4720:
4717:
4716:
4714:
4712:
4708:
4705:
4703:
4699:
4695:
4691:
4686:
4682:
4676:
4673:
4671:
4668:
4667:
4664:
4660:
4653:
4648:
4646:
4641:
4639:
4634:
4633:
4630:
4624:
4623:causaScientia
4620:
4618:
4614:
4611:
4608:
4604:
4601:
4600:
4592:
4591:0-521-83971-8
4588:
4584:
4580:
4578:
4574:
4570:
4566:
4561:
4556:
4552:
4548:
4544:
4540:
4535:
4532:
4528:
4524:
4520:
4516:
4511:
4507:
4503:
4499:
4495:
4491:
4487:
4486:
4478:
4473:
4470:
4466:
4462:
4458:
4454:
4450:
4446:
4442:
4438:
4434:
4432:
4431:0-534-24312-6
4428:
4424:
4420:
4419:Berger, R. L.
4416:
4413:
4412:
4401:
4397:
4392:
4387:
4383:
4379:
4375:
4370:
4366:
4360:
4356:
4352:
4348:
4344:
4340:
4336:
4332:
4328:
4324:
4320:
4316:
4312:
4308:
4307:
4301:
4297:
4293:
4289:
4283:
4279:
4275:
4271:
4267:
4263:
4259:
4255:
4249:
4245:
4241:
4236:
4232:
4227:
4226:
4220:
4219:Freedman, D.A
4216:
4213:
4212:90-272-3271-7
4209:
4205:
4201:
4197:
4193:
4192:Peirce, C. S.
4190:
4185:
4182:
4178:
4175:, v. 13, pp.
4174:
4170:
4167:
4164:
4160:
4157:, v. 13, pp.
4156:
4152:
4149:
4146:
4142:
4139:, v. 12, pp.
4138:
4134:
4131:
4128:
4124:
4120:
4116:
4115:
4113:
4112:
4107:
4106:Peirce, C. S.
4104:
4099:
4095:
4091:
4087:
4083:
4079:
4078:
4073:
4072:Neyman, Jerzy
4069:
4066:
4062:
4059:
4057:
4056:0-387-96307-3
4053:
4049:
4045:
4042:
4038:
4034:
4029:
4024:
4020:
4016:
4015:
4010:
4006:
4002:
3999:
3995:
3991:
3987:
3982:
3977:
3973:
3969:
3968:
3963:
3958:Reprinted as
3955:
3951:
3947:
3943:
3939:
3935:
3931:
3925:
3921:
3920:
3915:
3910:
3906:
3902:
3898:
3894:
3890:
3886:
3882:
3878:
3874:
3869:
3864:
3860:
3856:
3855:
3850:
3847:(June 2001).
3846:
3841:
3837:
3833:
3829:
3825:
3820:
3817:
3813:
3809:
3808:David Collier
3805:
3801:
3798:
3794:
3790:
3786:
3780:
3776:
3772:
3768:
3764:
3761:
3757:
3753:
3751:
3746:
3745:Fisher, R. A.
3743:
3740:
3739:0-521-68567-2
3736:
3732:
3728:
3724:
3721:
3717:
3713:
3709:
3703:
3699:
3698:Prentice Hall
3695:
3690:
3686:
3682:
3677:
3676:
3664:
3663:0-412-03471-9
3660:
3657:, CRC Press.
3656:
3655:
3650:
3645:
3637:
3631:
3627:
3623:
3619:
3611:
3607:
3603:
3599:
3592:
3584:
3580:
3576:
3572:
3568:
3564:
3559:
3554:
3550:
3546:
3542:
3535:
3527:
3523:
3519:
3517:0-07-021910-9
3513:
3509:
3508:
3500:
3492:
3488:
3484:
3482:0-471-27548-4
3478:
3474:
3473:
3465:
3459:
3455:
3449:
3440:
3433:
3428:
3419:
3413:Zabell (1992)
3410:
3404:Neyman (1956)
3401:
3392:
3383:
3381:
3371:
3369:
3359:
3357:
3347:
3339:
3335:
3331:
3327:
3322:
3317:
3313:
3309:
3302:
3294:
3290:
3286:
3282:
3278:
3274:
3270:
3266:
3262:
3258:
3251:
3242:
3234:
3230:
3225:
3220:
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3212:
3208:
3204:
3200:
3196:
3190:
3181:
3173:
3169:
3165:
3158:
3150:
3146:
3141:
3136:
3132:
3128:
3124:
3120:
3116:
3109:
3101:
3097:
3092:
3087:
3083:
3079:
3075:
3071:
3067:
3060:
3051:
3043:
3039:
3034:
3029:
3025:
3021:
3017:
3013:
3009:
3002:
2993:
2986:
2982:
2976:
2967:
2964:
2960:
2959:
2955:
2946:
2940:
2936:
2932:
2926:
2917:
2911:
2910:981-02-3111-3
2907:
2903:
2899:
2894:
2887:
2884:
2879:
2872:
2867:
2861:Peirce (1883)
2858:
2849:
2842:
2838:
2834:
2829:
2820:
2813:
2809:
2804:
2795:
2786:
2784:
2777:
2773:
2769:
2768:
2763:
2758:
2748:
2738:
2719:
2699:
2689:
2679:
2671:
2665:
2661:
2654:
2647:
2641:
2632:
2625:
2619:
2617:
2608:
2602:
2598:
2591:
2589:
2582:
2581:0-7619-2904-5
2578:
2574:
2568:
2561:
2557:
2552:
2543:
2536:
2535:0-521-78450-6
2532:
2528:
2522:
2514:
2512:9780716747420
2508:
2504:
2503:
2495:
2486:
2484:
2469:
2465:
2459:
2450:
2441:
2425:
2421:
2414:
2407:
2405:
2399:
2393:
2386:
2382:
2378:
2372:
2368:
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2343:
2333:
2330:
2328:
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2323:
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2313:
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2308:
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2303:
2300:
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2256:
2251:
2249:
2237:
2234:
2232:
2229:
2227:
2223:
2219:
2216:
2214:
2211:
2209:
2206:
2204:
2201:
2199:
2196:
2194:
2191:
2190:
2189:
2187:
2177:
2175:
2171:
2167:
2163:
2159:
2149:
2147:
2143:
2139:
2135:
2131:
2127:
2123:
2118:
2108:
2106:
2101:
2099:
2095:
2091:
2087:
2086:coding theory
2082:
2080:
2077:
2073:
2069:
2065:
2061:
2056:
2054:
2050:
2046:
2040:
2025:
2023:
2019:
2014:
2012:
2008:
2004:
2000:
1999:
1987:
1984:November 2017
1978:
1974:
1971:This section
1969:
1966:
1962:
1961:
1957:
1943:
1939:
1936:
1935:bootstrapping
1932:
1928:
1924:
1921:
1899:
1887:
1883:
1879:
1878:
1877:
1874:
1860:
1840:
1817:
1809:
1803:
1796:, denoted as
1795:
1791:
1790:Likelihoodism
1785:
1784:Likelihoodism
1775:
1773:
1769:
1765:
1761:
1757:
1752:
1748:
1742:
1740:
1735:
1721:
1720:Bayes factors
1718:
1716:
1712:
1709:
1708:
1702:
1700:
1693:
1683:
1681:
1677:
1672:
1670:
1669:least squares
1666:
1662:
1658:
1654:
1650:
1646:
1642:
1637:
1635:
1631:
1618:
1615:
1613:
1610:
1608:
1606:
1602:
1601:
1595:
1590:
1580:
1578:
1574:
1573:likelihoodist
1570:
1566:
1561:
1552:
1535:
1529:
1521:
1502:
1496:
1473:
1470:
1467:
1459:
1453:
1450:
1444:
1438:
1430:
1411:
1403:
1399:
1374:
1351:
1343:
1339:
1335:
1331:
1327:
1324:
1319:
1315:
1306:
1303:
1298:
1294:
1289:
1285:
1263:
1259:
1255:
1252:
1249:
1244:
1240:
1236:
1231:
1227:
1204:
1200:
1196:
1193:
1190:
1185:
1181:
1177:
1172:
1168:
1159:
1155:
1135:
1131:
1127:
1122:
1118:
1111:
1108:
1105:
1097:
1093:
1089:
1084:
1080:
1073:
1065:
1061:
1057:
1052:
1048:
1036:
1035:random design
1032:
1031:
1030:
1027:
1018:
1009:
1005:
1002:
999:
993:
992:information.
991:
987:
983:
979:
975:
971:
964:
960:
959:Random sample
954:
953:Randomization
944:
942:
938:
935:
931:
927:
926:biostatistics
923:
919:
915:
910:
906:
901:
899:
895:
891:
887:
883:
879:
875:
871:
867:
863:
859:
855:
850:
846:
842:
838:
828:
825:
820:
815:
807:
802:
789:
785:
781:
780:
776:
773:
769:
768:
764:
761:
757:
753:
752:
748:
747:
746:
738:
736:
731:
725:
721:
708:
704:
701:
698:
694:
691:
687:
684:
680:
676:
672:
668:
665:
661:
660:
659:
657:
653:
648:
646:
645:Sir David Cox
641:
639:
635:
631:
621:
619:
615:
611:
607:
604:(rather than
603:
599:
595:
591:
587:
583:
579:
577:
573:
569:
565:
561:
557:
556:data analysis
553:
539:
534:
532:
527:
525:
520:
519:
517:
516:
513:
510:
509:
500:
497:
495:
492:
490:
487:
486:
485:
482:
478:
475:
473:
472:Bibliometrics
470:
469:
468:
465:
463:
460:
458:
455:
454:
448:
447:
440:
437:
433:
430:
429:
428:
425:
421:
418:
416:
413:
411:
410:Meta-analysis
408:
406:
403:
401:
400:Bibliometrics
398:
397:
396:
393:
391:
388:
384:
381:
380:
379:
376:
374:
371:
369:
366:
364:
361:
359:
356:
352:
349:
347:
344:
342:
339:
338:
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327:
326:
325:
322:
320:
317:
315:
312:
310:
307:
305:
302:
300:
297:
296:
290:
289:
282:
279:
277:
274:
272:
271:Phenomenology
269:
267:
264:
262:
259:
257:
254:
252:
249:
247:
244:
242:
239:
237:
234:
232:
229:
228:
225:
220:
219:
210:
207:
205:
202:
201:
200:
197:
195:
192:
190:
187:
185:
182:
180:
177:
175:
172:
170:
167:
165:
162:
160:
157:
156:
153:
148:
147:
140:
137:
133:
130:
129:
128:
125:
123:
120:
118:
115:
114:
108:
107:
98:
95:
93:
90:
89:
88:
85:
83:
80:
78:
75:
73:
70:
69:
66:
61:
60:
56:
52:
51:
48:
45:
44:
40:
36:
35:
30:
19:
6940:
6928:
6909:
6902:
6814:Econometrics
6764: /
6747:Chemometrics
6724:Epidemiology
6717: /
6690:Applications
6532:ARIMA model
6479:Q-statistic
6428:Stationarity
6324:Multivariate
6267: /
6263: /
6261:Multivariate
6259: /
6199: /
6195: /
5969:Bayes factor
5868:Signed rank
5780:
5754:
5746:
5734:
5429:Completeness
5302:
5265:Cohort study
5163:Opinion poll
5098:Missing data
5085:Study design
5040:Scatter plot
4962:Scatter plot
4955:Spearman's Ï
4917:Grouped data
4582:
4542:
4538:
4518:
4514:
4489:
4483:
4468:
4444:
4440:
4422:
4381:
4377:
4354:
4310:
4304:
4273:
4239:
4229:. New York:
4224:
4195:
4180:
4172:
4162:
4154:
4144:
4136:
4126:
4118:
4109:
4081:
4075:
4064:
4061:Moore, D. S.
4050:, Springer.
4047:
4018:
4012:
3997:
3971:
3965:
3945:
3941:
3918:
3905:the original
3858:
3852:
3827:
3823:
3803:
3770:
3760:Abraham Wald
3756:Jerzy Neyman
3748:
3726:
3693:
3680:
3652:
3644:
3617:
3601:
3597:
3591:
3548:
3544:
3534:
3506:
3499:
3471:
3464:
3448:
3439:
3427:
3418:
3409:
3400:
3391:
3350:Soofi (2000)
3346:
3311:
3307:
3301:
3260:
3256:
3250:
3241:
3206:
3202:
3189:
3180:
3171:
3168:IMS Bulletin
3167:
3157:
3122:
3118:
3108:
3076:(2): 64â73.
3073:
3069:
3059:
3050:
3018:(2): 64â73.
3015:
3011:
3001:
2992:
2980:
2975:
2962:
2954:
2945:
2930:
2925:
2916:
2901:
2893:
2885:
2878:
2866:
2857:
2848:
2836:
2828:
2819:
2811:
2803:
2794:
2765:
2757:
2747:
2737:
2688:
2678:
2662:. Springer.
2659:
2653:
2645:
2640:
2631:
2623:
2596:
2572:
2567:
2559:
2551:
2546:Kruskal 1988
2542:
2526:
2521:
2501:
2494:
2471:. Retrieved
2467:
2458:
2449:
2440:
2428:. Retrieved
2423:
2413:
2403:
2401:
2392:
2376:
2371:
2346:
2254:
2252:
2245:
2185:
2183:
2174:group theory
2155:
2120:
2102:
2083:
2057:
2042:
2015:
2001:(AIC) is an
1996:
1994:
1981:
1977:adding to it
1972:
1875:
1787:
1767:
1743:
1730:
1695:
1673:
1638:
1627:
1604:
1592:
1562:
1558:
1519:
1428:
1390:
1157:
1034:
1028:
1024:
1015:
1006:
1003:
994:
980:ensures the
966:
922:econometrics
902:
851:
848:
823:
816:
812:
777:
765:
749:
744:
729:
727:
649:
642:
627:
624:Introduction
616:); see also
613:
612:(instead of
609:
605:
601:
597:
593:
581:
580:
571:
563:
551:
550:
367:
251:Hermeneutics
139:Quantitative
6970:Mathematics
6942:WikiProject
6857:Cartography
6819:Jurimetrics
6771:Reliability
6502:Time domain
6481:(LjungâBox)
6403:Time-series
6281:Categorical
6265:Time-series
6257:Categorical
6192:(Bernoulli)
6027:Correlation
6007:Correlation
5803:JarqueâBera
5775:Chi-squared
5537:M-estimator
5490:Asymptotics
5434:Sufficiency
5201:Interaction
5113:Replication
5093:Effect size
5050:Violin plot
5030:Radar chart
5010:Forest plot
5000:Correlogram
4950:Kendall's Ï
4471:(Springer).
4447:: 291â313.
4415:Casella, G.
4000:, Springer.
3948:: 369â375.
3806:(Edited by
3604:(1): 1â68.
3432:Hampel 2003
2259:probability
2098:data mining
1565:frequentist
937:likelihoods
934:log-concave
866:sample mean
679:probability
656:proposition
592:, the term
324:Ethnography
224:Methodology
179:Fallibilism
127:Qualitative
97:Referencing
6980:Categories
6809:Demography
6527:ARMA model
6332:Regression
5909:(Friedman)
5870:(Wilcoxon)
5808:Normality
5798:Lilliefors
5745:Student's
5621:Resampling
5495:Robustness
5483:divergence
5473:Efficiency
5411:(monotone)
5406:Likelihood
5323:Population
5156:Stratified
5108:Population
4927:Dependence
4883:Count data
4814:Percentile
4791:Dispersion
4724:Arithmetic
4659:Statistics
4242:. Berlin:
4225:Statistics
3752:, Series B
3723:Cox, D. R.
3321:2008.01006
3195:Neyman, J.
2963:Statistics
2812:Statistics
2810:et alia's
2762:Neyman, J.
2537:(page 341)
2473:2019-01-23
2430:26 October
2358:References
2332:Stylometry
2255:observable
2248:prediction
2226:regression
1918:, are the
1756:incoherent
1690:See also:
1676:estimators
957:See also:
896:, and the
799:See also:
703:clustering
697:hypothesis
652:conclusion
614:prediction
568:population
499:Statistics
494:Simulation
432:Simulation
373:Interviews
336:Experiment
304:Case study
276:Pragmatism
194:Pragmatism
184:Positivism
174:Empiricism
6190:Logistic
5957:posterior
5883:Rank sum
5631:Jackknife
5626:Bootstrap
5444:Bootstrap
5379:Parameter
5328:Statistic
5123:Statistic
5035:Run chart
5020:Pie chart
5015:Histogram
5005:Fan chart
4980:Bar chart
4862:L-moments
4749:Geometric
4521:: 102â7.
4492:: 69â91.
4343:120143121
4046:. (1986)
3863:CiteSeerX
3610:0365-320X
3575:0090-5364
3558:1301.1717
3338:220935477
3277:0003-1305
2933:, Wiley.
2898:Rao, C.R.
2833:Gelman A.
2404:inference
2402:The term
2363:Citations
2096:, and in
2003:estimator
1903:¯
1861:θ
1818:θ
1530:μ
1497:μ
1439:μ
1304:≤
1253:⋯
1194:⋯
1109:⋯
990:covariate
976:, use of
907:like the
788:Cox model
673:, e.g. a
634:selecting
610:inference
606:inference
594:inference
132:Art-based
6904:Category
6597:Survival
6474:Johansen
6197:Binomial
6152:Isotonic
5739:(normal)
5384:location
5191:Blocking
5146:Sampling
5025:QâQ plot
4990:Box plot
4972:Graphics
4867:Skewness
4857:Kurtosis
4829:Variance
4759:Heronian
4754:Harmonic
4609:platform
4569:17410547
4506:14136146
4421:(2002).
4272:(1989).
3916:(2008).
3901:14460386
3802:(2010).
3769:(2009).
3725:(2006).
3685:Elsevier
3583:88520957
3293:53505632
3285:27643780
3197:(1937).
3149:30460455
3100:30270947
3042:30270947
2281:See also
1760:coherent
1739:proposed
1569:Bayesian
630:sampling
602:learning
598:training
299:Analysis
92:Argument
82:Question
77:Proposal
47:Research
39:a series
37:Part of
6930:Commons
6877:Kriging
6762:Process
6719:studies
6578:Wavelet
6411:General
5578:Plug-in
5372:L space
5151:Cluster
4852:Moments
4670:Outline
4585:, CUP.
4400:2246073
4335:1825292
4327:2669786
4296:1082556
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