3739:(a particular unit of observation). For example, biologists may count the number of tree species in a forest: events would be tree observations, exposure would be unit area, and rate would be the number of species per unit area. Demographers may model death rates in geographic areas as the count of deaths divided by personâyears. More generally, event rates can be calculated as events per unit time, which allows the observation window to vary for each unit. In these examples, exposure is respectively unit area, personâyears and unit time. In Poisson regression this is handled as an
8037:
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394:
7416:
3743:. If the rate is count/exposure, multiplying both sides of the equation by exposure moves it to the right side of the equation. When both sides of the equation are then logged, the final model contains log(exposure) as a term that is added to the regression coefficients. This logged variable, log(exposure), is called the offset variable and enters on the right-hand side of the equation with a parameter estimate (for log(exposure)) constrained to 1.
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is a popular generalization of
Poisson regression because it loosens the highly restrictive assumption that the variance is equal to the mean made by the Poisson model. The traditional negative binomial regression model is based on the Poisson-gamma mixture distribution. This model is popular because
4159:
Another common problem with
Poisson regression is excess zeros: if there are two processes at work, one determining whether there are zero events or any events, and a Poisson process determining how many events there are, there will be more zeros than a Poisson regression would predict. An example
3432:
3726:
such as the arrival of a telephone call at a call centre. The events must be independent in the sense that the arrival of one call will not make another more or less likely, but the probability per unit time of events is understood to be related to covariates such as time of day.
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and indicates that the model is not appropriate. A common reason is the omission of relevant explanatory variables, or dependent observations. Under some circumstances, the problem of overdispersion can be solved by using
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and must be found by numerical methods. The probability surface for maximum-likelihood
Poisson regression is always concave, making NewtonâRaphson or other gradient-based methods appropriate estimation techniques.
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Ver Hoef and Boveng described the difference between quasi-Poisson (also called overdispersion with quasi-likelihood) and negative binomial (equivalent to gamma-Poisson) as follows: If
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3960:{\displaystyle \log \left({\frac {\operatorname {E} (Y\mid x)}{\text{exposure}}}\right)=\log(\operatorname {E} (Y\mid x))-\log({\text{exposure}})=\theta 'x-\log({\text{exposure}})}
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So the coefficient of the model is to be interpreted as the increase in the logarithm of the count of the outcome variable when the independent variable increases by 1.
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3037:{\displaystyle p(y_{1},\ldots ,y_{m}\mid x_{1},\ldots ,x_{m};\theta )=\prod _{i=1}^{m}{\frac {e^{y_{i}\theta 'x_{i}}e^{-e^{\theta 'x_{i}}}}{y_{i}!}}.}
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only appear in the first two terms of each term in the summation. Therefore, given that we are only interested in finding the best value for
3427:{\displaystyle \ell (\theta \mid X,Y)=\log L(\theta \mid X,Y)=\sum _{i=1}^{m}\left(y_{i}\theta 'x_{i}-e^{\theta 'x_{i}}-\log(y_{i}!)\right).}
2467:
7206:
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Poisson regression may also be appropriate for rate data, where the rate is a count of events divided by some measure of that unit's
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4156:. Ver Hoef and Boveng discussed an example where they selected between the two by plotting mean squared residuals vs. the mean.
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That is, when the independent variable increases by 1, the outcome variable is multiplied by the exponentiated coefficient.
1908:{\displaystyle \log \left({\dfrac {\operatorname {E} (Y_{2}\mid x_{2})}{\operatorname {E} (Y_{1}\mid x_{1})}}\right)=\beta }
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2681:{\displaystyle p(y\mid x;\theta )={\frac {\lambda ^{y}}{y!}}e^{-\lambda }={\frac {e^{y\theta 'x}e^{-e^{\theta 'x}}}{y!}}}
4160:
would be the distribution of cigarettes smoked in an hour by members of a group where some individuals are non-smokers.
8072:
7876:
7526:
4722:
1610:{\displaystyle \log(\operatorname {E} (Y_{2}\mid x_{2}))-\log(\operatorname {E} (Y_{1}\mid x_{1}))=\beta (x_{2}-x_{1})}
518:
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6853:
6745:
7602:
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288:
3218:{\displaystyle L(\theta \mid X,Y)=\prod _{i=1}^{m}{\frac {e^{y_{i}\theta 'x_{i}}e^{-e^{\theta 'x_{i}}}}{y_{i}!}}.}
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2021:{\displaystyle {\dfrac {\operatorname {E} (Y_{2}\mid x_{2})}{\operatorname {E} (Y_{1}\mid x_{1})}}=e^{\beta }}
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has not actually changed. A formula in this form is typically difficult to work with; instead, one uses the
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1782:{\displaystyle \log(\operatorname {E} (Y_{2}\mid x_{2}))-\log(\operatorname {E} (Y_{1}\mid x_{1}))=\beta }
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360:
4904:"Is eliciting dependency worth the effort? A study for the multivariate Poisson-Gamma probability model"
4064:
is that its mean is equal to its variance. In certain circumstances, it will be found that the observed
3584:{\displaystyle \ell (\theta \mid X,Y)=\sum _{i=1}^{m}\left(y_{i}\theta 'x_{i}-e^{\theta 'x_{i}}\right).}
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4560:{\displaystyle \sum _{i=1}^{m}\log(p(y_{i};e^{\theta 'x_{i}}))-\lambda \left\|\theta \right\|_{2}^{2},}
4396:
329:
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225:
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776:{\displaystyle \log(\operatorname {E} (Y\mid \mathbf {x} ))={\boldsymbol {\theta }}'\mathbf {x} ,\,}
631:{\displaystyle \log(\operatorname {E} (Y\mid \mathbf {x} ))=\alpha +\mathbf {\beta } '\mathbf {x} ,}
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that makes this probability as large as possible. To do this, the equation is first rewritten as a
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380:
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174:
67:
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986:{\displaystyle \operatorname {E} (Y\mid \mathbf {x} )=e^{{\boldsymbol {\theta }}'\mathbf {x} }.\,}
902:
789:
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7287:
6900:
6840:
6777:
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6137:
5999:
5989:
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5753:
2124:{\displaystyle \operatorname {E} (Y_{2}\mid x_{2})=e^{\beta }\operatorname {E} (Y_{1}\mid x_{1})}
410:
303:
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5307:
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4908:
Proceedings of the
Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
4851:"Quasi-Poisson vs. Negative Binomial Regression: How should we model overdispersed count data?"
1081:
267:
262:
204:
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4573:
4207:
When estimating the parameters for
Poisson regression, one typically tries to find values for
3722:
Poisson regression may be appropriate when the dependent variable is a count, for instance of
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7955:
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and substantial extra-Poisson variation, the negative binomial weights are capped at 1/
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5712:
5488:
5368:
5024:
4935:
Perperoglou, Aris (2011-09-08). "Fitting survival data with penalized
Poisson regression".
4862:
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4831:
4799:
4783:"Multiple routes to delinquency? A test of developmental and general theories of crime"
4782:
4763:
4672:
4649:"Log Linear Models for Contingency Tables: A Generalization of Classical Least Squares"
3048:
2246:
2226:
2206:
2149:
Often, the object of interest is the average partial effect or average marginal effect
1077:
398:
112:
2344:{\displaystyle {\frac {\partial E(Y|x)}{\partial x}}=\exp(\theta '\mathbb {x} )\beta }
8036:
7996:
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7302:
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5240:
5126:
Myers, Raymond H.; et al. (2010). "Logistic and
Poisson Regression Models".
5020:
4435:. Regularization can be added to this optimization problem by instead maximizing
370:
77:
6772:
7919:
7231:
7226:
5689:
5619:
5265:
4623:
Partial likelihood methods for panel data § Pooled QMLE for
Poisson models
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474:
122:
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4903:
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5275:
5270:
5009:. Springer Texts in Statistics (Second ed.). New York: Springer-Verlag.
4956:
4178:
On the contrary, underdispersion may pose an issue for parameter estimation.
3651:{\displaystyle {\frac {\partial \ell (\theta \mid X,Y)}{\partial \theta }}=0}
2354:
This can be estimated using the coefficient estimates from the
Poisson model
1455:{\displaystyle \log(\operatorname {E} (Y_{1}\mid x_{1}))=\alpha +\beta x_{1}}
1366:{\displaystyle \log(\operatorname {E} (Y_{2}\mid x_{2}))=\alpha +\beta x_{2}}
500:
241:
117:
1182:{\displaystyle \log(\operatorname {E} (Y\mid \mathbf {x} ))=\alpha +\beta x}
7330:
7263:
7240:
7155:
6485:
5781:
5679:
5614:
5556:
5541:
5478:
5433:
5128:
Generalized Linear Models With
Applications in Engineering and the Sciences
4882:
107:
7579:
7373:
7335:
7018:
6919:
6781:
6594:
6561:
6053:
5970:
5965:
5609:
5566:
5546:
5526:
5516:
5285:
4902:
Schwarzenegger, Rafael; Quigley, John; Walls, Lesley (23 November 2021).
4595:
3658:
which has no closed-form solution. However, the negative log-likelihood,
153:
102:
4628:
Control function (econometrics) § Endogeneity in Poisson regression
921:, the predicted mean of the associated Poisson distribution is given by
6219:
5699:
5399:
5330:
5280:
5255:
5175:
4767:
4676:
4653:
Journal of the Royal Statistical Society, Series C (Applied Statistics)
2830:, the probability of attaining this particular set of data is given by
454:
438:
4874:
2526:{\displaystyle \lambda :=\operatorname {E} (Y\mid x)=e^{\theta 'x},\,}
6372:
6224:
5844:
5639:
5551:
5536:
5531:
5496:
4814:
Berk R, MacDonald J (2008). "Overdispersion and Poisson regression".
4694:(2nd ed.). Cambridge, Massachusetts: The MIT Press. p. 726.
4186:
Poisson regression creates proportional hazards models, one class of
478:
470:
5037:"The Econometrics of Discrete Positive Variables: the Poisson Model"
4759:
4668:
2243:. The average partial effect in the Poisson model for a continuous
5888:
5506:
5383:
5378:
5373:
4065:
2412:{\displaystyle {\hat {\theta }}=({\hat {\alpha }},{\hat {\beta }})}
4739:
4648:
507:
function as the assumed probability distribution of the response.
7393:
7094:
4307:{\displaystyle \sum _{i=1}^{m}\log(p(y_{i};e^{\theta 'x_{i}})),}
2444:
7315:
6296:
6270:
6250:
5501:
5292:
5107:
Jones, Andrew M.; et al. (2013). "Models for count data".
492:
it models the Poisson heterogeneity with a gamma distribution.
5144:
5069:(8th ed.). Upper Saddle River: Prentice Hall. pp.
5235:
3803:{\displaystyle \log(\operatorname {E} (Y\mid x))=\theta 'x}
2761:{\displaystyle x_{i}\in \mathbb {R} ^{n+1},\,i=1,\ldots ,m}
816:
independent variables concatenated to the number one. Here
4901:
4211:
that maximize the likelihood of an expression of the form
1093:
Suppose we have a model with a single predictor, that is,
7500:
5043:. New York: Cambridge University Press. pp. 270â83.
5130:(Second ed.). New Jersey: Wiley. pp. 176â183.
4740:"The Analysis of Rates Using Poisson Regression Models"
4928:
4780:
4576:
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4399:
4327:
4220:
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3072:
2839:
2778:
2701:
2549:
2470:
2425:
2360:
2272:
2249:
2229:
2209:
2196:{\displaystyle {\frac {\partial E(Y|x)}{\partial x}}}
2155:
2037:
1926:
1924:
1816:
1804:
1675:
1626:
1474:
1382:
1293:
1244:
1198:
1128:
1099:
1062:
1033:
1002:
930:
905:
885:
862:
842:
822:
792:
715:
699:{\displaystyle \mathbf {\beta } \in \mathbb {R} ^{n}}
675:
647:
565:
521:
481:. A Poisson regression model is sometimes known as a
7057:
Autoregressive conditional heteroskedasticity (ARCH)
4692:
Econometric Analysis of Cross Section and Panel Data
4055:
2203:, which is interpreted as the change in the outcome
485:, especially when used to model contingency tables.
4813:
4128:. For both models, parameters are estimated using
4120:is the quasi-Poisson overdispersion parameter, and
2819:{\displaystyle y_{1},\ldots ,y_{m}\in \mathbb {N} }
461:. Poisson regression assumes the response variable
6519:
5062:
4983:
4706:
4582:
4559:
4427:
4377:
4306:
3959:
3802:
3730:
3694:
3650:
3583:
3426:
3217:
3036:
2818:
2760:
2691:Now suppose we are given a data set consisting of
2680:
2525:
2433:
2411:
2343:
2255:
2235:
2223:for a one unit change in the independent variable
2215:
2195:
2123:
2020:
1907:
1781:
1658:
1609:
1454:
1365:
1276:
1230:
1181:
1111:
1068:
1048:
1015:
985:
913:
891:
868:
848:
828:
800:
775:
698:
661:
630:
544:
477:can be modeled by a linear combination of unknown
8088:Mathematical and quantitative methods (economics)
4849:Ver Hoef, JAY M.; Boveng, Peter L. (2007-01-01).
4202:
3717:
2137:The exponentiated coefficient is also called the
1192:Suppose we compute the predicted values at point
1088:
812: + 1)-dimensional vector consisting of
8064:
5061:(2008). "Models for Event Counts and Duration".
4737:
3594:To find a maximum, we need to solve an equation
545:{\displaystyle \mathbf {x} \in \mathbb {R} ^{n}}
6605:Multivariate adaptive regression splines (MARS)
5041:Econometrics of Qualitative Dependent Variables
4321:is the number of examples in the data set, and
4981:
4848:
706:. Sometimes this is written more compactly as
7486:
5160:
4683:
4646:
2445:Maximum likelihood-based parameter estimation
418:
4068:is greater than the mean; this is known as
3710:can be applied to find the optimal value of
879:Thus, when given a Poisson regression model
5004:
4934:
4181:
1080:. The maximum-likelihood estimates lack a
7493:
7479:
5205:
5167:
5153:
5031:
4713:(Fifth ed.). Prentice-Hall. pp.
4689:
4378:{\displaystyle p(y_{i};e^{\theta 'x_{i}})}
4140:. For negative binomial, the weights are
1465:By subtracting the first from the second:
425:
411:
5818:
5007:Log-linear models and logistic regression
4919:
4798:
2812:
2736:
2717:
2522:
2427:
2331:
2144:
982:
772:
686:
655:
532:
3702:, is a convex function, and so standard
3051:, we wish to find the set of parameters
8002:Numerical smoothing and differentiation
5111:. London: Routledge. pp. 295â341.
4982:Cameron, A. C.; Trivedi, P. K. (1998).
3695:{\displaystyle -\ell (\theta \mid X,Y)}
1027:observations with corresponding values
964:
756:
662:{\displaystyle \alpha \in \mathbb {R} }
8065:
7131:KaplanâMeier estimator (product limit)
5057:
4937:Statistical Methods & Applications
4704:
4132:. For quasi-Poisson, the weights are
4096:, the quasi-Poisson model assumes var(
2826:. Then, for a given set of parameters
499:with the logarithm as the (canonical)
7474:
7204:
6771:
6518:
5817:
5587:
5204:
5148:
5125:
5106:
5087:
2536:and thus, the Poisson distribution's
1795:By applying the rules of logarithms:
7537:Iteratively reweighted least squares
7441:
7141:Accelerated failure time (AFT) model
4175:may function better in these cases.
4130:iteratively reweighted least squares
4104:while the gamma-Poisson assumes var(
510:
7453:
6736:Analysis of variance (ANOVA, anova)
5588:
4816:Journal of Quantitative Criminology
13:
7555:Pearson product-moment correlation
6831:CochranâMantelâHaenszel statistics
5457:Pearson product-moment correlation
4975:
4800:10.1111/j.1745-9125.1997.tb00870.x
3879:
3836:
3762:
3633:
3604:
2477:
2301:
2276:
2184:
2159:
2086:
2038:
1966:
1929:
1856:
1819:
1735:
1685:
1534:
1484:
1392:
1303:
1138:
931:
725:
575:
14:
8099:
4986:Regression analysis of count data
4428:{\displaystyle e^{\theta 'x_{i}}}
4056:Overdispersion and zero inflation
1056:of the predictor variables, then
8035:
7452:
7440:
7428:
7415:
7414:
7205:
4194:for descriptions of Cox models.
3228:Note that the expression on the
1154:
1049:{\displaystyle \mathbf {x} _{i}}
1036:
973:
947:
907:
794:
765:
741:
621:
591:
556:, then the model takes the form
523:
392:
16:Statistical model for count data
7090:Least-squares spectral analysis
4943:(4). Springer Nature: 451â462.
4781:Paternoster R, Brame R (1997).
2461:, as stated above, is given by
340:Least-squares spectral analysis
278:Generalized estimating equation
98:Multinomial logistic regression
73:Vector generalized linear model
6071:Mean-unbiased minimum-variance
5174:
5092:. Cambridge University Press.
4990:. Cambridge University Press.
4895:
4842:
4807:
4774:
4731:
4698:
4640:
4539:
4533:
4522:
4519:
4478:
4472:
4372:
4331:
4298:
4295:
4254:
4248:
4203:Regularized Poisson regression
4126:negative binomial distribution
4124:is the shape parameter of the
4079:negative binomial distribution
3954:
3946:
3920:
3912:
3900:
3897:
3885:
3876:
3854:
3842:
3783:
3780:
3768:
3759:
3718:Poisson regression in practice
3689:
3671:
3628:
3610:
3485:
3467:
3413:
3397:
3300:
3282:
3267:
3249:
3094:
3076:
2913:
2843:
2571:
2553:
2495:
2483:
2406:
2400:
2385:
2376:
2367:
2335:
2319:
2296:
2289:
2282:
2179:
2172:
2165:
2118:
2092:
2070:
2044:
1998:
1972:
1961:
1935:
1888:
1862:
1851:
1825:
1770:
1767:
1741:
1732:
1720:
1717:
1691:
1682:
1604:
1578:
1569:
1566:
1540:
1531:
1519:
1516:
1490:
1481:
1427:
1424:
1398:
1389:
1338:
1335:
1309:
1300:
1271:
1245:
1225:
1199:
1161:
1158:
1144:
1135:
1089:Interpretation of coefficients
951:
937:
748:
745:
731:
722:
598:
595:
581:
572:
495:Poisson regression models are
1:
8078:Categorical regression models
7384:Geographic information system
6600:Simultaneous equations models
4633:
4590:. This technique, similar to
4197:
1659:{\displaystyle x_{2}=x_{1}+1}
1277:{\displaystyle (Y_{1},x_{1})}
1231:{\displaystyle (Y_{2},x_{2})}
159:Nonlinear mixed-effects model
8025:Regression analysis category
7915:Response surface methodology
6567:Coefficient of determination
6178:Uniformly most powerful test
5090:Negative Binomial Regression
5005:Christensen, Ronald (1997).
4690:Wooldridge, Jeffrey (2010).
2457:, the mean of the predicted
2434:{\displaystyle \mathbb {x} }
2419:with the observed values of
914:{\displaystyle \mathbf {x} }
801:{\displaystyle \mathbf {x} }
489:Negative binomial regression
7:
7897:FrischâWaughâLovell theorem
7867:Mean and predicted response
7136:Proportional hazards models
7080:Spectral density estimation
7062:Vector autoregression (VAR)
6496:Maximum posterior estimator
5728:Randomized controlled trial
4705:Greene, William H. (2003).
4601:
4570:for some positive constant
4192:proportional hazards models
3437:Notice that the parameters
361:Mean and predicted response
10:
8104:
7547:Correlation and dependence
6896:Multivariate distributions
5316:Average absolute deviation
4618:Fixed-effect Poisson model
3978:can be achieved using the
2449:Given a set of parameters
154:Linear mixed-effects model
8073:Generalized linear models
8020:
7984:
7933:
7905:
7892:Minimum mean-square error
7859:
7805:
7779:Decomposition of variance
7777:
7742:
7701:
7683:Growth curve (statistics)
7670:
7652:Generalized least squares
7632:
7621:
7588:
7545:
7512:
7410:
7364:
7301:
7254:
7217:
7213:
7200:
7172:
7154:
7121:
7112:
7070:
7017:
6978:
6927:
6918:
6884:Structural equation model
6839:
6796:
6792:
6767:
6726:
6692:
6646:
6613:
6575:
6542:
6538:
6514:
6454:
6363:
6282:
6246:
6237:
6220:Score/Lagrange multiplier
6205:
6158:
6103:
6029:
6020:
5830:
5826:
5813:
5772:
5746:
5698:
5653:
5635:Sample size determination
5600:
5596:
5583:
5487:
5442:
5416:
5398:
5354:
5306:
5226:
5217:
5213:
5200:
5182:
4949:10.1007/s10260-011-0172-1
4921:10.1177/1748006X211059417
4828:10.1007/s10940-008-9048-4
4738:Frome, Edward L. (1983).
4387:probability mass function
4165:generalized linear models
2538:probability mass function
497:generalized linear models
320:Least absolute deviations
7750:Generalized linear model
7642:Simple linear regression
7532:Non-linear least squares
7514:Computational statistics
7379:Environmental statistics
6901:Elliptical distributions
6694:Generalized linear model
6623:Simple linear regression
6393:HodgesâLehmann estimator
5850:Probability distribution
5759:Stochastic approximation
5321:Coefficient of variation
5109:Applied Health Economics
4583:{\displaystyle \lambda }
4182:Use in survival analysis
4060:A characteristic of the
3984:
3970:Offset in the case of a
447:generalized linear model
68:Generalized linear model
7039:Cross-correlation (XCF)
6647:Non-standard predictors
6081:LehmannâScheffĂŠ theorem
5754:Adaptive clinical trial
1069:{\displaystyle \theta }
892:{\displaystyle \theta }
869:{\displaystyle \alpha }
829:{\displaystyle \theta }
8042:Mathematics portal
7966:Orthogonal polynomials
7792:Analysis of covariance
7657:Weighted least squares
7647:Ordinary least squares
7598:Ordinary least squares
7435:Mathematics portal
7256:Engineering statistics
7164:NelsonâAalen estimator
6741:Analysis of covariance
6628:Ordinary least squares
6552:Pearson product-moment
5956:Statistical functional
5867:Empirical distribution
5700:Controlled experiments
5429:Frequency distribution
5207:Descriptive statistics
4647:Nelder, J. A. (1974).
4584:
4561:
4465:
4429:
4379:
4308:
4241:
3961:
3804:
3696:
3652:
3585:
3511:
3428:
3326:
3219:
3120:
3038:
2939:
2820:
2768:, along with a set of
2762:
2682:
2527:
2435:
2413:
2345:
2257:
2237:
2217:
2197:
2145:Average partial effect
2125:
2022:
1909:
1783:
1660:
1611:
1456:
1367:
1278:
1232:
1183:
1113:
1082:closed-form expression
1070:
1050:
1017:
987:
915:
893:
870:
850:
849:{\displaystyle \beta }
830:
802:
777:
700:
663:
632:
546:
399:Mathematics portal
325:Iteratively reweighted
8007:System identification
7971:Chebyshev polynomials
7956:Numerical integration
7907:Design of experiments
7851:Regression validation
7678:Polynomial regression
7603:Partial least squares
7351:Population statistics
7293:System identification
7027:Autocorrelation (ACF)
6955:Exponential smoothing
6869:Discriminant analysis
6864:Canonical correlation
6728:Partition of variance
6590:Regression validation
6434:(JonckheereâTerpstra)
6333:Likelihood-ratio test
6022:Frequentist inference
5934:Locationâscale family
5855:Sampling distribution
5820:Statistical inference
5787:Cross-sectional study
5774:Observational studies
5733:Randomized experiment
5562:Stem-and-leaf display
5364:Central limit theorem
5088:Hilbe, J. M. (2007).
5033:GouriĂŠroux, Christian
4585:
4562:
4445:
4430:
4393:with the mean set to
4380:
4309:
4221:
3962:
3805:
3731:"Exposure" and offset
3697:
3653:
3586:
3491:
3429:
3306:
3220:
3100:
3039:
2919:
2821:
2763:
2683:
2528:
2436:
2414:
2346:
2258:
2238:
2218:
2198:
2126:
2023:
1910:
1784:
1661:
1612:
1457:
1368:
1279:
1233:
1184:
1114:
1071:
1051:
1018:
1016:{\displaystyle Y_{i}}
988:
916:
894:
871:
851:
831:
803:
778:
701:
664:
633:
554:independent variables
547:
356:Regression validation
335:Bayesian multivariate
52:Polynomial regression
8083:Poisson distribution
8012:Moving least squares
7951:Approximation theory
7887:Studentized residual
7877:Errors and residuals
7872:GaussâMarkov theorem
7787:Analysis of variance
7709:Nonlinear regression
7688:Segmented regression
7662:General linear model
7580:Confounding variable
7527:Linear least squares
7274:Probabilistic design
6859:Principal components
6702:Exponential families
6654:Nonlinear regression
6633:General linear model
6595:Mixed effects models
6585:Errors and residuals
6562:Confounding variable
6464:Bayesian probability
6442:Van der Waerden test
6432:Ordered alternative
6197:Multiple comparisons
6076:RaoâBlackwellization
6039:Estimating equations
5995:Statistical distance
5713:Factorial experiment
5246:Arithmetic-Geometric
5065:Econometric Analysis
4709:Econometric Analysis
4613:Poisson distribution
4574:
4442:
4397:
4391:Poisson distribution
4325:
4218:
4062:Poisson distribution
3820:
3750:
3662:
3598:
3461:
3243:
3070:
2837:
2776:
2699:
2547:
2468:
2459:Poisson distribution
2453:and an input vector
2423:
2358:
2270:
2263:can be shown to be:
2247:
2227:
2207:
2153:
2035:
1922:
1802:
1673:
1624:
1472:
1380:
1291:
1242:
1196:
1126:
1097:
1076:can be estimated by
1060:
1031:
1000:
928:
903:
899:and an input vector
883:
860:
840:
820:
790:
713:
673:
645:
563:
519:
505:Poisson distribution
467:Poisson distribution
381:GaussâMarkov theorem
376:Studentized residual
366:Errors and residuals
200:Principal components
170:Nonlinear regression
57:General linear model
8030:Statistics category
7961:Gaussian quadrature
7846:Model specification
7813:Stepwise regression
7671:Predictor structure
7608:Total least squares
7590:Regression analysis
7575:Partial correlation
7506:regression analysis
7346:Official statistics
7269:Methods engineering
6950:Seasonal adjustment
6718:Poisson regressions
6638:Bayesian regression
6577:Regression analysis
6557:Partial correlation
6529:Regression analysis
6128:Prediction interval
6123:Likelihood interval
6113:Confidence interval
6105:Interval estimation
6066:Unbiased estimators
5884:Model specification
5764:Up-and-down designs
5452:Partial correlation
5408:Index of dispersion
5326:Interquartile range
4867:2007Ecol...88.2766V
4608:Zero-inflated model
4553:
4173:zero-inflated model
3706:techniques such as
3704:convex optimization
3454:! and simply write
3057:likelihood function
1112:{\displaystyle n=1}
451:regression analysis
226:Errors-in-variables
93:Logistic regression
83:Binomial regression
28:Regression analysis
22:Part of a series on
8047:Statistics outline
7946:Numerical analysis
7366:Spatial statistics
7246:Medical statistics
7146:First hitting time
7100:Whittle likelihood
6751:Degrees of freedom
6746:Multivariate ANOVA
6679:Heteroscedasticity
6491:Bayesian estimator
6456:Bayesian inference
6305:KolmogorovâSmirnov
6190:Randomization test
6160:Testing hypotheses
6133:Tolerance interval
6044:Maximum likelihood
5939:Exponential family
5872:Density estimation
5832:Statistical theory
5792:Natural experiment
5738:Scientific control
5655:Survey methodology
5341:Standard deviation
5059:Greene, William H.
4580:
4557:
4531:
4425:
4375:
4304:
3957:
3800:
3692:
3648:
3581:
3424:
3215:
3049:maximum likelihood
3034:
2816:
2758:
2678:
2523:
2431:
2409:
2341:
2253:
2233:
2213:
2193:
2121:
2018:
2003:
1905:
1893:
1779:
1656:
1607:
1452:
1363:
1274:
1228:
1179:
1109:
1078:maximum likelihood
1066:
1046:
1013:
983:
911:
889:
866:
846:
826:
798:
773:
696:
659:
628:
542:
469:, and assumes the
459:contingency tables
443:Poisson regression
113:Multinomial probit
8060:
8059:
8052:Statistics topics
7997:Calibration curve
7806:Model exploration
7773:
7772:
7743:Non-normal errors
7634:Linear regression
7625:statistical model
7468:
7467:
7406:
7405:
7402:
7401:
7341:National accounts
7311:Actuarial science
7303:Social statistics
7196:
7195:
7192:
7191:
7188:
7187:
7123:Survival function
7108:
7107:
6970:Granger causality
6811:Contingency table
6786:Survival analysis
6763:
6762:
6759:
6758:
6615:Linear regression
6510:
6509:
6506:
6505:
6481:Credible interval
6450:
6449:
6233:
6232:
6049:Method of moments
5918:Parametric family
5879:Statistical model
5809:
5808:
5805:
5804:
5723:Random assignment
5645:Statistical power
5579:
5578:
5575:
5574:
5424:Contingency table
5394:
5393:
5261:Generalized/power
5137:978-0-470-45463-3
5118:978-0-415-67682-3
5099:978-0-521-85772-7
5080:978-0-13-600383-0
5050:978-0-521-58985-7
5016:978-0-387-98247-2
4997:978-0-521-63201-0
4875:10.1890/07-0043.1
4861:(11): 2766â2772.
4188:survival analysis
4169:negative binomial
3952:
3918:
3861:
3860:
3640:
3210:
3047:By the method of
3029:
2676:
2597:
2403:
2388:
2370:
2308:
2256:{\displaystyle x}
2236:{\displaystyle x}
2216:{\displaystyle Y}
2191:
2002:
1892:
1620:Suppose now that
511:Regression models
435:
434:
88:Binary regression
47:Simple regression
42:Linear regression
8095:
8040:
8039:
7797:Multivariate AOV
7693:Local regression
7630:
7629:
7622:Regression as a
7613:Ridge regression
7560:Rank correlation
7495:
7488:
7481:
7472:
7471:
7456:
7455:
7444:
7443:
7433:
7432:
7418:
7417:
7321:Crime statistics
7215:
7214:
7202:
7201:
7119:
7118:
7085:Fourier analysis
7072:Frequency domain
7052:
6999:
6965:Structural break
6925:
6924:
6874:Cluster analysis
6821:Log-linear model
6794:
6793:
6769:
6768:
6710:
6684:Homoscedasticity
6540:
6539:
6516:
6515:
6435:
6427:
6419:
6418:(KruskalâWallis)
6403:
6388:
6343:Cross validation
6328:
6310:AndersonâDarling
6257:
6244:
6243:
6215:Likelihood-ratio
6207:Parametric tests
6185:Permutation test
6168:1- & 2-tails
6059:Minimum distance
6031:Point estimation
6027:
6026:
5978:Optimal decision
5929:
5828:
5827:
5815:
5814:
5797:Quasi-experiment
5747:Adaptive designs
5598:
5597:
5585:
5584:
5462:Rank correlation
5224:
5223:
5215:
5214:
5202:
5201:
5169:
5162:
5155:
5146:
5145:
5141:
5122:
5103:
5084:
5068:
5054:
5028:
5001:
4989:
4969:
4968:
4932:
4926:
4925:
4923:
4899:
4893:
4892:
4890:
4889:
4846:
4840:
4839:
4811:
4805:
4804:
4802:
4778:
4772:
4771:
4735:
4729:
4728:
4712:
4702:
4696:
4695:
4687:
4681:
4680:
4644:
4592:ridge regression
4589:
4587:
4586:
4581:
4566:
4564:
4563:
4558:
4552:
4547:
4542:
4518:
4517:
4516:
4515:
4506:
4490:
4489:
4464:
4459:
4434:
4432:
4431:
4426:
4424:
4423:
4422:
4421:
4412:
4384:
4382:
4381:
4376:
4371:
4370:
4369:
4368:
4359:
4343:
4342:
4313:
4311:
4310:
4305:
4294:
4293:
4292:
4291:
4282:
4266:
4265:
4240:
4235:
4144:/(1 +
4077:estimation or a
4075:quasi-likelihood
4051:
4048:
4045:
4042:
4039:
4036:
4033:
4030:
4027:
4024:
4021:
4018:
4015:
4012:
4009:
4006:
4003:
4000:
3997:
3994:
3991:
3988:
3981:
3966:
3964:
3963:
3958:
3953:
3950:
3933:
3919:
3916:
3866:
3862:
3858:
3857:
3834:
3809:
3807:
3806:
3801:
3796:
3708:gradient descent
3701:
3699:
3698:
3693:
3657:
3655:
3654:
3649:
3641:
3639:
3631:
3602:
3590:
3588:
3587:
3582:
3577:
3573:
3572:
3571:
3570:
3569:
3560:
3544:
3543:
3534:
3526:
3525:
3510:
3505:
3445:we may drop the
3433:
3431:
3430:
3425:
3420:
3416:
3409:
3408:
3387:
3386:
3385:
3384:
3375:
3359:
3358:
3349:
3341:
3340:
3325:
3320:
3224:
3222:
3221:
3216:
3211:
3209:
3205:
3204:
3194:
3193:
3192:
3191:
3190:
3189:
3188:
3179:
3158:
3157:
3156:
3155:
3146:
3138:
3137:
3122:
3119:
3114:
3043:
3041:
3040:
3035:
3030:
3028:
3024:
3023:
3013:
3012:
3011:
3010:
3009:
3008:
3007:
2998:
2977:
2976:
2975:
2974:
2965:
2957:
2956:
2941:
2938:
2933:
2906:
2905:
2887:
2886:
2874:
2873:
2855:
2854:
2825:
2823:
2822:
2817:
2815:
2807:
2806:
2788:
2787:
2767:
2765:
2764:
2759:
2732:
2731:
2720:
2711:
2710:
2687:
2685:
2684:
2679:
2677:
2675:
2667:
2666:
2665:
2664:
2663:
2659:
2638:
2637:
2633:
2616:
2611:
2610:
2598:
2596:
2588:
2587:
2578:
2532:
2530:
2529:
2524:
2518:
2517:
2513:
2440:
2438:
2437:
2432:
2430:
2418:
2416:
2415:
2410:
2405:
2404:
2396:
2390:
2389:
2381:
2372:
2371:
2363:
2350:
2348:
2347:
2342:
2334:
2329:
2309:
2307:
2299:
2292:
2274:
2262:
2260:
2259:
2254:
2242:
2240:
2239:
2234:
2222:
2220:
2219:
2214:
2202:
2200:
2199:
2194:
2192:
2190:
2182:
2175:
2157:
2130:
2128:
2127:
2122:
2117:
2116:
2104:
2103:
2085:
2084:
2069:
2068:
2056:
2055:
2027:
2025:
2024:
2019:
2017:
2016:
2004:
2001:
1997:
1996:
1984:
1983:
1964:
1960:
1959:
1947:
1946:
1927:
1914:
1912:
1911:
1906:
1898:
1894:
1891:
1887:
1886:
1874:
1873:
1854:
1850:
1849:
1837:
1836:
1817:
1788:
1786:
1785:
1780:
1766:
1765:
1753:
1752:
1716:
1715:
1703:
1702:
1665:
1663:
1662:
1657:
1649:
1648:
1636:
1635:
1616:
1614:
1613:
1608:
1603:
1602:
1590:
1589:
1565:
1564:
1552:
1551:
1515:
1514:
1502:
1501:
1461:
1459:
1458:
1453:
1451:
1450:
1423:
1422:
1410:
1409:
1372:
1370:
1369:
1364:
1362:
1361:
1334:
1333:
1321:
1320:
1283:
1281:
1280:
1275:
1270:
1269:
1257:
1256:
1237:
1235:
1234:
1229:
1224:
1223:
1211:
1210:
1188:
1186:
1185:
1180:
1157:
1118:
1116:
1115:
1110:
1075:
1073:
1072:
1067:
1055:
1053:
1052:
1047:
1045:
1044:
1039:
1022:
1020:
1019:
1014:
1012:
1011:
992:
990:
989:
984:
978:
977:
976:
971:
967:
950:
920:
918:
917:
912:
910:
898:
896:
895:
890:
875:
873:
872:
867:
856:concatenated to
855:
853:
852:
847:
835:
833:
832:
827:
807:
805:
804:
799:
797:
782:
780:
779:
774:
768:
763:
759:
744:
705:
703:
702:
697:
695:
694:
689:
680:
668:
666:
665:
660:
658:
637:
635:
634:
629:
624:
619:
615:
594:
551:
549:
548:
543:
541:
540:
535:
526:
483:log-linear model
427:
420:
413:
397:
396:
304:Ridge regression
139:Multilevel model
19:
18:
8103:
8102:
8098:
8097:
8096:
8094:
8093:
8092:
8063:
8062:
8061:
8056:
8034:
8016:
7980:
7976:Chebyshev nodes
7929:
7925:Bayesian design
7901:
7882:Goodness of fit
7855:
7828:
7818:Model selection
7801:
7769:
7738:
7697:
7666:
7623:
7617:
7584:
7541:
7508:
7499:
7469:
7464:
7427:
7398:
7360:
7297:
7283:quality control
7250:
7232:Clinical trials
7209:
7184:
7168:
7156:Hazard function
7150:
7104:
7066:
7050:
7013:
7009:BreuschâGodfrey
6997:
6974:
6914:
6889:Factor analysis
6835:
6816:Graphical model
6788:
6755:
6722:
6708:
6688:
6642:
6609:
6571:
6534:
6533:
6502:
6446:
6433:
6425:
6417:
6401:
6386:
6365:Rank statistics
6359:
6338:Model selection
6326:
6284:Goodness of fit
6278:
6255:
6229:
6201:
6154:
6099:
6088:Median unbiased
6016:
5927:
5860:Order statistic
5822:
5801:
5768:
5742:
5694:
5649:
5592:
5590:Data collection
5571:
5483:
5438:
5412:
5390:
5350:
5302:
5219:Continuous data
5209:
5196:
5178:
5173:
5138:
5119:
5100:
5081:
5051:
5017:
4998:
4978:
4976:Further reading
4973:
4972:
4933:
4929:
4900:
4896:
4887:
4885:
4847:
4843:
4812:
4808:
4779:
4775:
4760:10.2307/2531094
4736:
4732:
4725:
4703:
4699:
4688:
4684:
4669:10.2307/2347125
4645:
4641:
4636:
4604:
4575:
4572:
4571:
4548:
4543:
4532:
4511:
4507:
4499:
4498:
4494:
4485:
4481:
4460:
4449:
4443:
4440:
4439:
4417:
4413:
4405:
4404:
4400:
4398:
4395:
4394:
4364:
4360:
4352:
4351:
4347:
4338:
4334:
4326:
4323:
4322:
4287:
4283:
4275:
4274:
4270:
4261:
4257:
4236:
4225:
4219:
4216:
4215:
4205:
4200:
4184:
4148:). With large
4112:(1 +
4058:
4053:
4052:
4049:
4046:
4043:
4040:
4037:
4034:
4031:
4028:
4025:
4022:
4019:
4016:
4013:
4010:
4007:
4004:
4001:
3998:
3995:
3992:
3989:
3986:
3979:
3949:
3926:
3915:
3835:
3833:
3829:
3821:
3818:
3817:
3789:
3751:
3748:
3747:
3733:
3720:
3663:
3660:
3659:
3632:
3603:
3601:
3599:
3596:
3595:
3565:
3561:
3553:
3552:
3548:
3539:
3535:
3527:
3521:
3517:
3516:
3512:
3506:
3495:
3462:
3459:
3458:
3453:
3404:
3400:
3380:
3376:
3368:
3367:
3363:
3354:
3350:
3342:
3336:
3332:
3331:
3327:
3321:
3310:
3244:
3241:
3240:
3230:right hand side
3200:
3196:
3195:
3184:
3180:
3172:
3171:
3167:
3163:
3159:
3151:
3147:
3139:
3133:
3129:
3128:
3124:
3123:
3121:
3115:
3104:
3071:
3068:
3067:
3019:
3015:
3014:
3003:
2999:
2991:
2990:
2986:
2982:
2978:
2970:
2966:
2958:
2952:
2948:
2947:
2943:
2942:
2940:
2934:
2923:
2901:
2897:
2882:
2878:
2869:
2865:
2850:
2846:
2838:
2835:
2834:
2811:
2802:
2798:
2783:
2779:
2777:
2774:
2773:
2721:
2716:
2715:
2706:
2702:
2700:
2697:
2696:
2668:
2652:
2651:
2647:
2643:
2639:
2626:
2622:
2618:
2617:
2615:
2603:
2599:
2589:
2583:
2579:
2577:
2548:
2545:
2544:
2506:
2505:
2501:
2469:
2466:
2465:
2447:
2426:
2424:
2421:
2420:
2395:
2394:
2380:
2379:
2362:
2361:
2359:
2356:
2355:
2330:
2322:
2300:
2288:
2275:
2273:
2271:
2268:
2267:
2248:
2245:
2244:
2228:
2225:
2224:
2208:
2205:
2204:
2183:
2171:
2158:
2156:
2154:
2151:
2150:
2147:
2139:incidence ratio
2112:
2108:
2099:
2095:
2080:
2076:
2064:
2060:
2051:
2047:
2036:
2033:
2032:
2012:
2008:
1992:
1988:
1979:
1975:
1965:
1955:
1951:
1942:
1938:
1928:
1925:
1923:
1920:
1919:
1882:
1878:
1869:
1865:
1855:
1845:
1841:
1832:
1828:
1818:
1815:
1811:
1803:
1800:
1799:
1761:
1757:
1748:
1744:
1711:
1707:
1698:
1694:
1674:
1671:
1670:
1644:
1640:
1631:
1627:
1625:
1622:
1621:
1598:
1594:
1585:
1581:
1560:
1556:
1547:
1543:
1510:
1506:
1497:
1493:
1473:
1470:
1469:
1446:
1442:
1418:
1414:
1405:
1401:
1381:
1378:
1377:
1357:
1353:
1329:
1325:
1316:
1312:
1292:
1289:
1288:
1265:
1261:
1252:
1248:
1243:
1240:
1239:
1219:
1215:
1206:
1202:
1197:
1194:
1193:
1153:
1127:
1124:
1123:
1098:
1095:
1094:
1091:
1061:
1058:
1057:
1040:
1035:
1034:
1032:
1029:
1028:
1007:
1003:
1001:
998:
997:
972:
963:
962:
961:
957:
946:
929:
926:
925:
906:
904:
901:
900:
884:
881:
880:
861:
858:
857:
841:
838:
837:
821:
818:
817:
793:
791:
788:
787:
764:
755:
754:
740:
714:
711:
710:
690:
685:
684:
676:
674:
671:
670:
654:
646:
643:
642:
620:
611:
610:
590:
564:
561:
560:
552:is a vector of
536:
531:
530:
522:
520:
517:
516:
513:
431:
391:
371:Goodness of fit
78:Discrete choice
17:
12:
11:
5:
8101:
8091:
8090:
8085:
8080:
8075:
8058:
8057:
8055:
8054:
8049:
8044:
8032:
8027:
8021:
8018:
8017:
8015:
8014:
8009:
8004:
7999:
7994:
7988:
7986:
7982:
7981:
7979:
7978:
7973:
7968:
7963:
7958:
7953:
7948:
7942:
7940:
7931:
7930:
7928:
7927:
7922:
7920:Optimal design
7917:
7911:
7909:
7903:
7902:
7900:
7899:
7894:
7889:
7884:
7879:
7874:
7869:
7863:
7861:
7857:
7856:
7854:
7853:
7848:
7843:
7842:
7841:
7836:
7831:
7826:
7815:
7809:
7807:
7803:
7802:
7800:
7799:
7794:
7789:
7783:
7781:
7775:
7774:
7771:
7770:
7768:
7767:
7762:
7757:
7752:
7746:
7744:
7740:
7739:
7737:
7736:
7731:
7726:
7721:
7719:Semiparametric
7716:
7711:
7705:
7703:
7699:
7698:
7696:
7695:
7690:
7685:
7680:
7674:
7672:
7668:
7667:
7665:
7664:
7659:
7654:
7649:
7644:
7638:
7636:
7627:
7619:
7618:
7616:
7615:
7610:
7605:
7600:
7594:
7592:
7586:
7585:
7583:
7582:
7577:
7572:
7566:
7564:Spearman's rho
7557:
7551:
7549:
7543:
7542:
7540:
7539:
7534:
7529:
7524:
7518:
7516:
7510:
7509:
7498:
7497:
7490:
7483:
7475:
7466:
7465:
7463:
7462:
7450:
7438:
7424:
7411:
7408:
7407:
7404:
7403:
7400:
7399:
7397:
7396:
7391:
7386:
7381:
7376:
7370:
7368:
7362:
7361:
7359:
7358:
7353:
7348:
7343:
7338:
7333:
7328:
7323:
7318:
7313:
7307:
7305:
7299:
7298:
7296:
7295:
7290:
7285:
7276:
7271:
7266:
7260:
7258:
7252:
7251:
7249:
7248:
7243:
7238:
7229:
7227:Bioinformatics
7223:
7221:
7211:
7210:
7198:
7197:
7194:
7193:
7190:
7189:
7186:
7185:
7183:
7182:
7176:
7174:
7170:
7169:
7167:
7166:
7160:
7158:
7152:
7151:
7149:
7148:
7143:
7138:
7133:
7127:
7125:
7116:
7110:
7109:
7106:
7105:
7103:
7102:
7097:
7092:
7087:
7082:
7076:
7074:
7068:
7067:
7065:
7064:
7059:
7054:
7046:
7041:
7036:
7035:
7034:
7032:partial (PACF)
7023:
7021:
7015:
7014:
7012:
7011:
7006:
7001:
6993:
6988:
6982:
6980:
6979:Specific tests
6976:
6975:
6973:
6972:
6967:
6962:
6957:
6952:
6947:
6942:
6937:
6931:
6929:
6922:
6916:
6915:
6913:
6912:
6911:
6910:
6909:
6908:
6893:
6892:
6891:
6881:
6879:Classification
6876:
6871:
6866:
6861:
6856:
6851:
6845:
6843:
6837:
6836:
6834:
6833:
6828:
6826:McNemar's test
6823:
6818:
6813:
6808:
6802:
6800:
6790:
6789:
6765:
6764:
6761:
6760:
6757:
6756:
6754:
6753:
6748:
6743:
6738:
6732:
6730:
6724:
6723:
6721:
6720:
6704:
6698:
6696:
6690:
6689:
6687:
6686:
6681:
6676:
6671:
6666:
6664:Semiparametric
6661:
6656:
6650:
6648:
6644:
6643:
6641:
6640:
6635:
6630:
6625:
6619:
6617:
6611:
6610:
6608:
6607:
6602:
6597:
6592:
6587:
6581:
6579:
6573:
6572:
6570:
6569:
6564:
6559:
6554:
6548:
6546:
6536:
6535:
6532:
6531:
6526:
6520:
6512:
6511:
6508:
6507:
6504:
6503:
6501:
6500:
6499:
6498:
6488:
6483:
6478:
6477:
6476:
6471:
6460:
6458:
6452:
6451:
6448:
6447:
6445:
6444:
6439:
6438:
6437:
6429:
6421:
6405:
6402:(MannâWhitney)
6397:
6396:
6395:
6382:
6381:
6380:
6369:
6367:
6361:
6360:
6358:
6357:
6356:
6355:
6350:
6345:
6335:
6330:
6327:(ShapiroâWilk)
6322:
6317:
6312:
6307:
6302:
6294:
6288:
6286:
6280:
6279:
6277:
6276:
6268:
6259:
6247:
6241:
6239:Specific tests
6235:
6234:
6231:
6230:
6228:
6227:
6222:
6217:
6211:
6209:
6203:
6202:
6200:
6199:
6194:
6193:
6192:
6182:
6181:
6180:
6170:
6164:
6162:
6156:
6155:
6153:
6152:
6151:
6150:
6145:
6135:
6130:
6125:
6120:
6115:
6109:
6107:
6101:
6100:
6098:
6097:
6092:
6091:
6090:
6085:
6084:
6083:
6078:
6063:
6062:
6061:
6056:
6051:
6046:
6035:
6033:
6024:
6018:
6017:
6015:
6014:
6009:
6004:
6003:
6002:
5992:
5987:
5986:
5985:
5975:
5974:
5973:
5968:
5963:
5953:
5948:
5943:
5942:
5941:
5936:
5931:
5915:
5914:
5913:
5908:
5903:
5893:
5892:
5891:
5886:
5876:
5875:
5874:
5864:
5863:
5862:
5852:
5847:
5842:
5836:
5834:
5824:
5823:
5811:
5810:
5807:
5806:
5803:
5802:
5800:
5799:
5794:
5789:
5784:
5778:
5776:
5770:
5769:
5767:
5766:
5761:
5756:
5750:
5748:
5744:
5743:
5741:
5740:
5735:
5730:
5725:
5720:
5715:
5710:
5704:
5702:
5696:
5695:
5693:
5692:
5690:Standard error
5687:
5682:
5677:
5676:
5675:
5670:
5659:
5657:
5651:
5650:
5648:
5647:
5642:
5637:
5632:
5627:
5622:
5620:Optimal design
5617:
5612:
5606:
5604:
5594:
5593:
5581:
5580:
5577:
5576:
5573:
5572:
5570:
5569:
5564:
5559:
5554:
5549:
5544:
5539:
5534:
5529:
5524:
5519:
5514:
5509:
5504:
5499:
5493:
5491:
5485:
5484:
5482:
5481:
5476:
5475:
5474:
5469:
5459:
5454:
5448:
5446:
5440:
5439:
5437:
5436:
5431:
5426:
5420:
5418:
5417:Summary tables
5414:
5413:
5411:
5410:
5404:
5402:
5396:
5395:
5392:
5391:
5389:
5388:
5387:
5386:
5381:
5376:
5366:
5360:
5358:
5352:
5351:
5349:
5348:
5343:
5338:
5333:
5328:
5323:
5318:
5312:
5310:
5304:
5303:
5301:
5300:
5295:
5290:
5289:
5288:
5283:
5278:
5273:
5268:
5263:
5258:
5253:
5251:Contraharmonic
5248:
5243:
5232:
5230:
5221:
5211:
5210:
5198:
5197:
5195:
5194:
5189:
5183:
5180:
5179:
5172:
5171:
5164:
5157:
5149:
5143:
5142:
5136:
5123:
5117:
5104:
5098:
5085:
5079:
5055:
5049:
5029:
5015:
5002:
4996:
4977:
4974:
4971:
4970:
4927:
4894:
4841:
4822:(3): 269â284.
4806:
4773:
4730:
4724:978-0130661890
4723:
4697:
4682:
4638:
4637:
4635:
4632:
4631:
4630:
4625:
4620:
4615:
4610:
4603:
4600:
4579:
4568:
4567:
4556:
4551:
4546:
4541:
4538:
4535:
4530:
4527:
4524:
4521:
4514:
4510:
4505:
4502:
4497:
4493:
4488:
4484:
4480:
4477:
4474:
4471:
4468:
4463:
4458:
4455:
4452:
4448:
4420:
4416:
4411:
4408:
4403:
4374:
4367:
4363:
4358:
4355:
4350:
4346:
4341:
4337:
4333:
4330:
4315:
4314:
4303:
4300:
4297:
4290:
4286:
4281:
4278:
4273:
4269:
4264:
4260:
4256:
4253:
4250:
4247:
4244:
4239:
4234:
4231:
4228:
4224:
4204:
4201:
4199:
4196:
4183:
4180:
4070:overdispersion
4057:
4054:
3985:
3968:
3967:
3956:
3948:
3945:
3942:
3939:
3936:
3932:
3929:
3925:
3922:
3914:
3911:
3908:
3905:
3902:
3899:
3896:
3893:
3890:
3887:
3884:
3881:
3878:
3875:
3872:
3869:
3865:
3856:
3853:
3850:
3847:
3844:
3841:
3838:
3832:
3828:
3825:
3813:which implies
3811:
3810:
3799:
3795:
3792:
3788:
3785:
3782:
3779:
3776:
3773:
3770:
3767:
3764:
3761:
3758:
3755:
3732:
3729:
3719:
3716:
3691:
3688:
3685:
3682:
3679:
3676:
3673:
3670:
3667:
3647:
3644:
3638:
3635:
3630:
3627:
3624:
3621:
3618:
3615:
3612:
3609:
3606:
3592:
3591:
3580:
3576:
3568:
3564:
3559:
3556:
3551:
3547:
3542:
3538:
3533:
3530:
3524:
3520:
3515:
3509:
3504:
3501:
3498:
3494:
3490:
3487:
3484:
3481:
3478:
3475:
3472:
3469:
3466:
3449:
3435:
3434:
3423:
3419:
3415:
3412:
3407:
3403:
3399:
3396:
3393:
3390:
3383:
3379:
3374:
3371:
3366:
3362:
3357:
3353:
3348:
3345:
3339:
3335:
3330:
3324:
3319:
3316:
3313:
3309:
3305:
3302:
3299:
3296:
3293:
3290:
3287:
3284:
3281:
3278:
3275:
3272:
3269:
3266:
3263:
3260:
3257:
3254:
3251:
3248:
3234:log-likelihood
3226:
3225:
3214:
3208:
3203:
3199:
3187:
3183:
3178:
3175:
3170:
3166:
3162:
3154:
3150:
3145:
3142:
3136:
3132:
3127:
3118:
3113:
3110:
3107:
3103:
3099:
3096:
3093:
3090:
3087:
3084:
3081:
3078:
3075:
3045:
3044:
3033:
3027:
3022:
3018:
3006:
3002:
2997:
2994:
2989:
2985:
2981:
2973:
2969:
2964:
2961:
2955:
2951:
2946:
2937:
2932:
2929:
2926:
2922:
2918:
2915:
2912:
2909:
2904:
2900:
2896:
2893:
2890:
2885:
2881:
2877:
2872:
2868:
2864:
2861:
2858:
2853:
2849:
2845:
2842:
2814:
2810:
2805:
2801:
2797:
2794:
2791:
2786:
2782:
2757:
2754:
2751:
2748:
2745:
2742:
2739:
2735:
2730:
2727:
2724:
2719:
2714:
2709:
2705:
2689:
2688:
2674:
2671:
2662:
2658:
2655:
2650:
2646:
2642:
2636:
2632:
2629:
2625:
2621:
2614:
2609:
2606:
2602:
2595:
2592:
2586:
2582:
2576:
2573:
2570:
2567:
2564:
2561:
2558:
2555:
2552:
2534:
2533:
2521:
2516:
2512:
2509:
2504:
2500:
2497:
2494:
2491:
2488:
2485:
2482:
2479:
2476:
2473:
2446:
2443:
2429:
2408:
2402:
2399:
2393:
2387:
2384:
2378:
2375:
2369:
2366:
2352:
2351:
2340:
2337:
2333:
2328:
2325:
2321:
2318:
2315:
2312:
2306:
2303:
2298:
2295:
2291:
2287:
2284:
2281:
2278:
2252:
2232:
2212:
2189:
2186:
2181:
2178:
2174:
2170:
2167:
2164:
2161:
2146:
2143:
2132:
2131:
2120:
2115:
2111:
2107:
2102:
2098:
2094:
2091:
2088:
2083:
2079:
2075:
2072:
2067:
2063:
2059:
2054:
2050:
2046:
2043:
2040:
2029:
2028:
2015:
2011:
2007:
2000:
1995:
1991:
1987:
1982:
1978:
1974:
1971:
1968:
1963:
1958:
1954:
1950:
1945:
1941:
1937:
1934:
1931:
1916:
1915:
1904:
1901:
1897:
1890:
1885:
1881:
1877:
1872:
1868:
1864:
1861:
1858:
1853:
1848:
1844:
1840:
1835:
1831:
1827:
1824:
1821:
1814:
1810:
1807:
1790:
1789:
1778:
1775:
1772:
1769:
1764:
1760:
1756:
1751:
1747:
1743:
1740:
1737:
1734:
1731:
1728:
1725:
1722:
1719:
1714:
1710:
1706:
1701:
1697:
1693:
1690:
1687:
1684:
1681:
1678:
1655:
1652:
1647:
1643:
1639:
1634:
1630:
1618:
1617:
1606:
1601:
1597:
1593:
1588:
1584:
1580:
1577:
1574:
1571:
1568:
1563:
1559:
1555:
1550:
1546:
1542:
1539:
1536:
1533:
1530:
1527:
1524:
1521:
1518:
1513:
1509:
1505:
1500:
1496:
1492:
1489:
1486:
1483:
1480:
1477:
1463:
1462:
1449:
1445:
1441:
1438:
1435:
1432:
1429:
1426:
1421:
1417:
1413:
1408:
1404:
1400:
1397:
1394:
1391:
1388:
1385:
1374:
1373:
1360:
1356:
1352:
1349:
1346:
1343:
1340:
1337:
1332:
1328:
1324:
1319:
1315:
1311:
1308:
1305:
1302:
1299:
1296:
1273:
1268:
1264:
1260:
1255:
1251:
1247:
1227:
1222:
1218:
1214:
1209:
1205:
1201:
1190:
1189:
1178:
1175:
1172:
1169:
1166:
1163:
1160:
1156:
1152:
1149:
1146:
1143:
1140:
1137:
1134:
1131:
1108:
1105:
1102:
1090:
1087:
1065:
1043:
1038:
1010:
1006:
994:
993:
981:
975:
970:
966:
960:
956:
953:
949:
945:
942:
939:
936:
933:
909:
888:
865:
845:
825:
796:
784:
783:
771:
767:
762:
758:
753:
750:
747:
743:
739:
736:
733:
730:
727:
724:
721:
718:
693:
688:
683:
679:
657:
653:
650:
639:
638:
627:
623:
618:
614:
609:
606:
603:
600:
597:
593:
589:
586:
583:
580:
577:
574:
571:
568:
539:
534:
529:
525:
512:
509:
475:expected value
453:used to model
433:
432:
430:
429:
422:
415:
407:
404:
403:
402:
401:
386:
385:
384:
383:
378:
373:
368:
363:
358:
350:
349:
345:
344:
343:
342:
337:
332:
327:
322:
314:
313:
312:
311:
306:
301:
296:
291:
283:
282:
281:
280:
275:
270:
265:
257:
256:
255:
254:
249:
244:
236:
235:
231:
230:
229:
228:
220:
219:
218:
217:
212:
207:
202:
197:
192:
187:
182:
180:Semiparametric
177:
172:
164:
163:
162:
161:
156:
151:
149:Random effects
146:
141:
133:
132:
131:
130:
125:
123:Ordered probit
120:
115:
110:
105:
100:
95:
90:
85:
80:
75:
70:
62:
61:
60:
59:
54:
49:
44:
36:
35:
31:
30:
24:
23:
15:
9:
6:
4:
3:
2:
8100:
8089:
8086:
8084:
8081:
8079:
8076:
8074:
8071:
8070:
8068:
8053:
8050:
8048:
8045:
8043:
8038:
8033:
8031:
8028:
8026:
8023:
8022:
8019:
8013:
8010:
8008:
8005:
8003:
8000:
7998:
7995:
7993:
7992:Curve fitting
7990:
7989:
7987:
7983:
7977:
7974:
7972:
7969:
7967:
7964:
7962:
7959:
7957:
7954:
7952:
7949:
7947:
7944:
7943:
7941:
7939:
7938:approximation
7936:
7932:
7926:
7923:
7921:
7918:
7916:
7913:
7912:
7910:
7908:
7904:
7898:
7895:
7893:
7890:
7888:
7885:
7883:
7880:
7878:
7875:
7873:
7870:
7868:
7865:
7864:
7862:
7858:
7852:
7849:
7847:
7844:
7840:
7837:
7835:
7832:
7830:
7829:
7821:
7820:
7819:
7816:
7814:
7811:
7810:
7808:
7804:
7798:
7795:
7793:
7790:
7788:
7785:
7784:
7782:
7780:
7776:
7766:
7763:
7761:
7758:
7756:
7753:
7751:
7748:
7747:
7745:
7741:
7735:
7732:
7730:
7727:
7725:
7722:
7720:
7717:
7715:
7714:Nonparametric
7712:
7710:
7707:
7706:
7704:
7700:
7694:
7691:
7689:
7686:
7684:
7681:
7679:
7676:
7675:
7673:
7669:
7663:
7660:
7658:
7655:
7653:
7650:
7648:
7645:
7643:
7640:
7639:
7637:
7635:
7631:
7628:
7626:
7620:
7614:
7611:
7609:
7606:
7604:
7601:
7599:
7596:
7595:
7593:
7591:
7587:
7581:
7578:
7576:
7573:
7570:
7569:Kendall's tau
7567:
7565:
7561:
7558:
7556:
7553:
7552:
7550:
7548:
7544:
7538:
7535:
7533:
7530:
7528:
7525:
7523:
7522:Least squares
7520:
7519:
7517:
7515:
7511:
7507:
7503:
7502:Least squares
7496:
7491:
7489:
7484:
7482:
7477:
7476:
7473:
7461:
7460:
7451:
7449:
7448:
7439:
7437:
7436:
7431:
7425:
7423:
7422:
7413:
7412:
7409:
7395:
7392:
7390:
7389:Geostatistics
7387:
7385:
7382:
7380:
7377:
7375:
7372:
7371:
7369:
7367:
7363:
7357:
7356:Psychometrics
7354:
7352:
7349:
7347:
7344:
7342:
7339:
7337:
7334:
7332:
7329:
7327:
7324:
7322:
7319:
7317:
7314:
7312:
7309:
7308:
7306:
7304:
7300:
7294:
7291:
7289:
7286:
7284:
7280:
7277:
7275:
7272:
7270:
7267:
7265:
7262:
7261:
7259:
7257:
7253:
7247:
7244:
7242:
7239:
7237:
7233:
7230:
7228:
7225:
7224:
7222:
7220:
7219:Biostatistics
7216:
7212:
7208:
7203:
7199:
7181:
7180:Log-rank test
7178:
7177:
7175:
7171:
7165:
7162:
7161:
7159:
7157:
7153:
7147:
7144:
7142:
7139:
7137:
7134:
7132:
7129:
7128:
7126:
7124:
7120:
7117:
7115:
7111:
7101:
7098:
7096:
7093:
7091:
7088:
7086:
7083:
7081:
7078:
7077:
7075:
7073:
7069:
7063:
7060:
7058:
7055:
7053:
7051:(BoxâJenkins)
7047:
7045:
7042:
7040:
7037:
7033:
7030:
7029:
7028:
7025:
7024:
7022:
7020:
7016:
7010:
7007:
7005:
7004:DurbinâWatson
7002:
7000:
6994:
6992:
6989:
6987:
6986:DickeyâFuller
6984:
6983:
6981:
6977:
6971:
6968:
6966:
6963:
6961:
6960:Cointegration
6958:
6956:
6953:
6951:
6948:
6946:
6943:
6941:
6938:
6936:
6935:Decomposition
6933:
6932:
6930:
6926:
6923:
6921:
6917:
6907:
6904:
6903:
6902:
6899:
6898:
6897:
6894:
6890:
6887:
6886:
6885:
6882:
6880:
6877:
6875:
6872:
6870:
6867:
6865:
6862:
6860:
6857:
6855:
6852:
6850:
6847:
6846:
6844:
6842:
6838:
6832:
6829:
6827:
6824:
6822:
6819:
6817:
6814:
6812:
6809:
6807:
6806:Cohen's kappa
6804:
6803:
6801:
6799:
6795:
6791:
6787:
6783:
6779:
6775:
6770:
6766:
6752:
6749:
6747:
6744:
6742:
6739:
6737:
6734:
6733:
6731:
6729:
6725:
6719:
6715:
6711:
6705:
6703:
6700:
6699:
6697:
6695:
6691:
6685:
6682:
6680:
6677:
6675:
6672:
6670:
6667:
6665:
6662:
6660:
6659:Nonparametric
6657:
6655:
6652:
6651:
6649:
6645:
6639:
6636:
6634:
6631:
6629:
6626:
6624:
6621:
6620:
6618:
6616:
6612:
6606:
6603:
6601:
6598:
6596:
6593:
6591:
6588:
6586:
6583:
6582:
6580:
6578:
6574:
6568:
6565:
6563:
6560:
6558:
6555:
6553:
6550:
6549:
6547:
6545:
6541:
6537:
6530:
6527:
6525:
6522:
6521:
6517:
6513:
6497:
6494:
6493:
6492:
6489:
6487:
6484:
6482:
6479:
6475:
6472:
6470:
6467:
6466:
6465:
6462:
6461:
6459:
6457:
6453:
6443:
6440:
6436:
6430:
6428:
6422:
6420:
6414:
6413:
6412:
6409:
6408:Nonparametric
6406:
6404:
6398:
6394:
6391:
6390:
6389:
6383:
6379:
6378:Sample median
6376:
6375:
6374:
6371:
6370:
6368:
6366:
6362:
6354:
6351:
6349:
6346:
6344:
6341:
6340:
6339:
6336:
6334:
6331:
6329:
6323:
6321:
6318:
6316:
6313:
6311:
6308:
6306:
6303:
6301:
6299:
6295:
6293:
6290:
6289:
6287:
6285:
6281:
6275:
6273:
6269:
6267:
6265:
6260:
6258:
6253:
6249:
6248:
6245:
6242:
6240:
6236:
6226:
6223:
6221:
6218:
6216:
6213:
6212:
6210:
6208:
6204:
6198:
6195:
6191:
6188:
6187:
6186:
6183:
6179:
6176:
6175:
6174:
6171:
6169:
6166:
6165:
6163:
6161:
6157:
6149:
6146:
6144:
6141:
6140:
6139:
6136:
6134:
6131:
6129:
6126:
6124:
6121:
6119:
6116:
6114:
6111:
6110:
6108:
6106:
6102:
6096:
6093:
6089:
6086:
6082:
6079:
6077:
6074:
6073:
6072:
6069:
6068:
6067:
6064:
6060:
6057:
6055:
6052:
6050:
6047:
6045:
6042:
6041:
6040:
6037:
6036:
6034:
6032:
6028:
6025:
6023:
6019:
6013:
6010:
6008:
6005:
6001:
5998:
5997:
5996:
5993:
5991:
5988:
5984:
5983:loss function
5981:
5980:
5979:
5976:
5972:
5969:
5967:
5964:
5962:
5959:
5958:
5957:
5954:
5952:
5949:
5947:
5944:
5940:
5937:
5935:
5932:
5930:
5924:
5921:
5920:
5919:
5916:
5912:
5909:
5907:
5904:
5902:
5899:
5898:
5897:
5894:
5890:
5887:
5885:
5882:
5881:
5880:
5877:
5873:
5870:
5869:
5868:
5865:
5861:
5858:
5857:
5856:
5853:
5851:
5848:
5846:
5843:
5841:
5838:
5837:
5835:
5833:
5829:
5825:
5821:
5816:
5812:
5798:
5795:
5793:
5790:
5788:
5785:
5783:
5780:
5779:
5777:
5775:
5771:
5765:
5762:
5760:
5757:
5755:
5752:
5751:
5749:
5745:
5739:
5736:
5734:
5731:
5729:
5726:
5724:
5721:
5719:
5716:
5714:
5711:
5709:
5706:
5705:
5703:
5701:
5697:
5691:
5688:
5686:
5685:Questionnaire
5683:
5681:
5678:
5674:
5671:
5669:
5666:
5665:
5664:
5661:
5660:
5658:
5656:
5652:
5646:
5643:
5641:
5638:
5636:
5633:
5631:
5628:
5626:
5623:
5621:
5618:
5616:
5613:
5611:
5608:
5607:
5605:
5603:
5599:
5595:
5591:
5586:
5582:
5568:
5565:
5563:
5560:
5558:
5555:
5553:
5550:
5548:
5545:
5543:
5540:
5538:
5535:
5533:
5530:
5528:
5525:
5523:
5520:
5518:
5515:
5513:
5512:Control chart
5510:
5508:
5505:
5503:
5500:
5498:
5495:
5494:
5492:
5490:
5486:
5480:
5477:
5473:
5470:
5468:
5465:
5464:
5463:
5460:
5458:
5455:
5453:
5450:
5449:
5447:
5445:
5441:
5435:
5432:
5430:
5427:
5425:
5422:
5421:
5419:
5415:
5409:
5406:
5405:
5403:
5401:
5397:
5385:
5382:
5380:
5377:
5375:
5372:
5371:
5370:
5367:
5365:
5362:
5361:
5359:
5357:
5353:
5347:
5344:
5342:
5339:
5337:
5334:
5332:
5329:
5327:
5324:
5322:
5319:
5317:
5314:
5313:
5311:
5309:
5305:
5299:
5296:
5294:
5291:
5287:
5284:
5282:
5279:
5277:
5274:
5272:
5269:
5267:
5264:
5262:
5259:
5257:
5254:
5252:
5249:
5247:
5244:
5242:
5239:
5238:
5237:
5234:
5233:
5231:
5229:
5225:
5222:
5220:
5216:
5212:
5208:
5203:
5199:
5193:
5190:
5188:
5185:
5184:
5181:
5177:
5170:
5165:
5163:
5158:
5156:
5151:
5150:
5147:
5139:
5133:
5129:
5124:
5120:
5114:
5110:
5105:
5101:
5095:
5091:
5086:
5082:
5076:
5072:
5067:
5066:
5060:
5056:
5052:
5046:
5042:
5038:
5034:
5030:
5026:
5022:
5018:
5012:
5008:
5003:
4999:
4993:
4988:
4987:
4980:
4979:
4966:
4962:
4958:
4954:
4950:
4946:
4942:
4938:
4931:
4922:
4917:
4913:
4909:
4905:
4898:
4884:
4880:
4876:
4872:
4868:
4864:
4860:
4856:
4852:
4845:
4837:
4833:
4829:
4825:
4821:
4817:
4810:
4801:
4796:
4792:
4788:
4784:
4777:
4769:
4765:
4761:
4757:
4753:
4749:
4745:
4741:
4734:
4726:
4720:
4716:
4711:
4710:
4701:
4693:
4686:
4678:
4674:
4670:
4666:
4662:
4658:
4654:
4650:
4643:
4639:
4629:
4626:
4624:
4621:
4619:
4616:
4614:
4611:
4609:
4606:
4605:
4599:
4597:
4594:, can reduce
4593:
4577:
4554:
4549:
4544:
4536:
4528:
4525:
4512:
4508:
4503:
4500:
4495:
4491:
4486:
4482:
4475:
4469:
4466:
4461:
4456:
4453:
4450:
4446:
4438:
4437:
4436:
4418:
4414:
4409:
4406:
4401:
4392:
4388:
4365:
4361:
4356:
4353:
4348:
4344:
4339:
4335:
4328:
4320:
4301:
4288:
4284:
4279:
4276:
4271:
4267:
4262:
4258:
4251:
4245:
4242:
4237:
4232:
4229:
4226:
4222:
4214:
4213:
4212:
4210:
4195:
4193:
4189:
4179:
4176:
4174:
4170:
4166:
4161:
4157:
4155:
4151:
4147:
4143:
4139:
4135:
4131:
4127:
4123:
4119:
4115:
4111:
4107:
4103:
4099:
4095:
4091:
4087:
4082:
4080:
4076:
4071:
4067:
4063:
3983:
3977:
3973:
3943:
3940:
3937:
3934:
3930:
3927:
3923:
3909:
3906:
3903:
3894:
3891:
3888:
3882:
3873:
3870:
3867:
3863:
3851:
3848:
3845:
3839:
3830:
3826:
3823:
3816:
3815:
3814:
3797:
3793:
3790:
3786:
3777:
3774:
3771:
3765:
3756:
3753:
3746:
3745:
3744:
3742:
3738:
3728:
3725:
3715:
3713:
3709:
3705:
3686:
3683:
3680:
3677:
3674:
3668:
3665:
3645:
3642:
3636:
3625:
3622:
3619:
3616:
3613:
3607:
3578:
3574:
3566:
3562:
3557:
3554:
3549:
3545:
3540:
3536:
3531:
3528:
3522:
3518:
3513:
3507:
3502:
3499:
3496:
3492:
3488:
3482:
3479:
3476:
3473:
3470:
3464:
3457:
3456:
3455:
3452:
3448:
3444:
3440:
3421:
3417:
3410:
3405:
3401:
3394:
3391:
3388:
3381:
3377:
3372:
3369:
3364:
3360:
3355:
3351:
3346:
3343:
3337:
3333:
3328:
3322:
3317:
3314:
3311:
3307:
3303:
3297:
3294:
3291:
3288:
3285:
3279:
3276:
3273:
3270:
3264:
3261:
3258:
3255:
3252:
3246:
3239:
3238:
3237:
3235:
3231:
3212:
3206:
3201:
3197:
3185:
3181:
3176:
3173:
3168:
3164:
3160:
3152:
3148:
3143:
3140:
3134:
3130:
3125:
3116:
3111:
3108:
3105:
3101:
3097:
3091:
3088:
3085:
3082:
3079:
3073:
3066:
3065:
3064:
3062:
3058:
3054:
3050:
3031:
3025:
3020:
3016:
3004:
3000:
2995:
2992:
2987:
2983:
2979:
2971:
2967:
2962:
2959:
2953:
2949:
2944:
2935:
2930:
2927:
2924:
2920:
2916:
2910:
2907:
2902:
2898:
2894:
2891:
2888:
2883:
2879:
2875:
2870:
2866:
2862:
2859:
2856:
2851:
2847:
2840:
2833:
2832:
2831:
2829:
2808:
2803:
2799:
2795:
2792:
2789:
2784:
2780:
2771:
2755:
2752:
2749:
2746:
2743:
2740:
2737:
2733:
2728:
2725:
2722:
2712:
2707:
2703:
2694:
2672:
2669:
2660:
2656:
2653:
2648:
2644:
2640:
2634:
2630:
2627:
2623:
2619:
2612:
2607:
2604:
2600:
2593:
2590:
2584:
2580:
2574:
2568:
2565:
2562:
2559:
2556:
2550:
2543:
2542:
2541:
2539:
2519:
2514:
2510:
2507:
2502:
2498:
2492:
2489:
2486:
2480:
2474:
2471:
2464:
2463:
2462:
2460:
2456:
2452:
2442:
2397:
2391:
2382:
2373:
2364:
2338:
2326:
2323:
2316:
2313:
2310:
2304:
2293:
2285:
2279:
2266:
2265:
2264:
2250:
2230:
2210:
2187:
2176:
2168:
2162:
2142:
2140:
2135:
2113:
2109:
2105:
2100:
2096:
2089:
2081:
2077:
2073:
2065:
2061:
2057:
2052:
2048:
2041:
2031:
2030:
2013:
2009:
2005:
1993:
1989:
1985:
1980:
1976:
1969:
1956:
1952:
1948:
1943:
1939:
1932:
1918:
1917:
1902:
1899:
1895:
1883:
1879:
1875:
1870:
1866:
1859:
1846:
1842:
1838:
1833:
1829:
1822:
1812:
1808:
1805:
1798:
1797:
1796:
1793:
1776:
1773:
1762:
1758:
1754:
1749:
1745:
1738:
1729:
1726:
1723:
1712:
1708:
1704:
1699:
1695:
1688:
1679:
1676:
1669:
1668:
1667:
1666:. We obtain:
1653:
1650:
1645:
1641:
1637:
1632:
1628:
1599:
1595:
1591:
1586:
1582:
1575:
1572:
1561:
1557:
1553:
1548:
1544:
1537:
1528:
1525:
1522:
1511:
1507:
1503:
1498:
1494:
1487:
1478:
1475:
1468:
1467:
1466:
1447:
1443:
1439:
1436:
1433:
1430:
1419:
1415:
1411:
1406:
1402:
1395:
1386:
1383:
1376:
1375:
1358:
1354:
1350:
1347:
1344:
1341:
1330:
1326:
1322:
1317:
1313:
1306:
1297:
1294:
1287:
1286:
1285:
1266:
1262:
1258:
1253:
1249:
1220:
1216:
1212:
1207:
1203:
1176:
1173:
1170:
1167:
1164:
1150:
1147:
1141:
1132:
1129:
1122:
1121:
1120:
1106:
1103:
1100:
1086:
1083:
1079:
1063:
1041:
1026:
1008:
1004:
979:
968:
958:
954:
943:
940:
934:
924:
923:
922:
886:
877:
863:
843:
823:
815:
811:
769:
760:
751:
737:
734:
728:
719:
716:
709:
708:
707:
691:
681:
677:
651:
648:
625:
616:
612:
607:
604:
601:
587:
584:
578:
569:
566:
559:
558:
557:
555:
537:
527:
508:
506:
502:
501:link function
498:
493:
490:
486:
484:
480:
476:
472:
468:
464:
460:
456:
452:
448:
444:
440:
428:
423:
421:
416:
414:
409:
408:
406:
405:
400:
395:
390:
389:
388:
387:
382:
379:
377:
374:
372:
369:
367:
364:
362:
359:
357:
354:
353:
352:
351:
347:
346:
341:
338:
336:
333:
331:
328:
326:
323:
321:
318:
317:
316:
315:
310:
307:
305:
302:
300:
297:
295:
292:
290:
287:
286:
285:
284:
279:
276:
274:
271:
269:
266:
264:
261:
260:
259:
258:
253:
250:
248:
245:
243:
242:Least squares
240:
239:
238:
237:
233:
232:
227:
224:
223:
222:
221:
216:
213:
211:
208:
206:
203:
201:
198:
196:
193:
191:
188:
186:
183:
181:
178:
176:
175:Nonparametric
173:
171:
168:
167:
166:
165:
160:
157:
155:
152:
150:
147:
145:
144:Fixed effects
142:
140:
137:
136:
135:
134:
129:
126:
124:
121:
119:
118:Ordered logit
116:
114:
111:
109:
106:
104:
101:
99:
96:
94:
91:
89:
86:
84:
81:
79:
76:
74:
71:
69:
66:
65:
64:
63:
58:
55:
53:
50:
48:
45:
43:
40:
39:
38:
37:
33:
32:
29:
26:
25:
21:
20:
7985:Applications
7824:
7759:
7702:Non-standard
7457:
7445:
7426:
7419:
7331:Econometrics
7281: /
7264:Chemometrics
7241:Epidemiology
7234: /
7207:Applications
7049:ARIMA model
6996:Q-statistic
6945:Stationarity
6841:Multivariate
6820:
6784: /
6780: /
6778:Multivariate
6776: /
6717:
6716: /
6712: /
6486:Bayes factor
6385:Signed rank
6297:
6271:
6263:
6251:
5946:Completeness
5782:Cohort study
5680:Opinion poll
5615:Missing data
5602:Study design
5557:Scatter plot
5479:Scatter plot
5472:Spearman's Ď
5434:Grouped data
5127:
5108:
5089:
5064:
5040:
5006:
4985:
4940:
4936:
4930:
4911:
4907:
4897:
4886:. Retrieved
4858:
4854:
4844:
4819:
4815:
4809:
4790:
4786:
4776:
4751:
4747:
4743:
4733:
4708:
4700:
4691:
4685:
4660:
4656:
4652:
4642:
4569:
4318:
4316:
4208:
4206:
4185:
4177:
4167:such as the
4162:
4158:
4153:
4149:
4145:
4141:
4137:
4133:
4121:
4117:
4113:
4109:
4105:
4101:
4097:
4093:
4089:
4085:
4083:
4059:
3969:
3812:
3740:
3736:
3734:
3721:
3711:
3593:
3450:
3446:
3442:
3438:
3436:
3233:
3227:
3060:
3059:in terms of
3052:
3046:
2827:
2769:
2692:
2690:
2540:is given by
2535:
2454:
2450:
2448:
2353:
2148:
2138:
2136:
2133:
1794:
1791:
1619:
1464:
1191:
1092:
995:
878:
813:
809:
785:
640:
514:
494:
488:
487:
462:
442:
436:
299:Non-negative
127:
7459:WikiProject
7374:Cartography
7336:Jurimetrics
7288:Reliability
7019:Time domain
6998:(LjungâBox)
6920:Time-series
6798:Categorical
6782:Time-series
6774:Categorical
6709:(Bernoulli)
6544:Correlation
6524:Correlation
6320:JarqueâBera
6292:Chi-squared
6054:M-estimator
6007:Asymptotics
5951:Sufficiency
5718:Interaction
5630:Replication
5610:Effect size
5567:Violin plot
5547:Radar chart
5527:Forest plot
5517:Correlogram
5467:Kendall's Ď
4787:Criminology
4596:overfitting
1025:independent
808:is now an (
309:Regularized
273:Generalized
205:Least angle
103:Mixed logit
8067:Categories
7860:Background
7823:Mallows's
7326:Demography
7044:ARMA model
6849:Regression
6426:(Friedman)
6387:(Wilcoxon)
6325:Normality
6315:Lilliefors
6262:Student's
6138:Resampling
6012:Robustness
6000:divergence
5990:Efficiency
5928:(monotone)
5923:Likelihood
5840:Population
5673:Stratified
5625:Population
5444:Dependence
5400:Count data
5331:Percentile
5308:Dispersion
5241:Arithmetic
5176:Statistics
4888:2016-09-01
4744:Biometrics
4634:References
4198:Extensions
4081:instead.
3982:function:
836:is simply
503:, and the
479:parameters
455:count data
439:statistics
348:Background
252:Non-linear
234:Estimation
7935:Numerical
6707:Logistic
6474:posterior
6400:Rank sum
6148:Jackknife
6143:Bootstrap
5961:Bootstrap
5896:Parameter
5845:Statistic
5640:Statistic
5552:Run chart
5537:Pie chart
5532:Histogram
5522:Fan chart
5497:Bar chart
5379:L-moments
5266:Geometric
4957:1618-2510
4836:121273486
4793:: 45â84.
4754:665â674.
4663:323â329.
4578:λ
4537:θ
4529:λ
4526:−
4501:θ
4470:
4447:∑
4407:θ
4354:θ
4277:θ
4246:
4223:∑
4171:model or
4116:), where
3944:
3938:−
3928:θ
3910:
3904:−
3892:∣
3883:
3874:
3849:∣
3840:
3827:
3791:θ
3775:∣
3766:
3757:
3678:∣
3675:θ
3669:ℓ
3666:−
3637:θ
3634:∂
3617:∣
3614:θ
3608:ℓ
3605:∂
3555:θ
3546:−
3529:θ
3493:∑
3474:∣
3471:θ
3465:ℓ
3395:
3389:−
3370:θ
3361:−
3344:θ
3308:∑
3289:∣
3286:θ
3277:
3256:∣
3253:θ
3247:ℓ
3174:θ
3165:−
3141:θ
3102:∏
3083:∣
3080:θ
2993:θ
2984:−
2960:θ
2921:∏
2911:θ
2892:…
2876:∣
2860:…
2809:∈
2793:…
2750:…
2713:∈
2654:θ
2645:−
2628:θ
2608:λ
2605:−
2581:λ
2569:θ
2560:∣
2508:θ
2490:∣
2481:
2472:λ
2401:^
2398:β
2386:^
2383:α
2368:^
2365:θ
2339:β
2324:θ
2317:
2302:∂
2277:∂
2185:∂
2160:∂
2106:∣
2090:
2082:β
2058:∣
2042:
2014:β
1986:∣
1970:
1949:∣
1933:
1903:β
1876:∣
1860:
1839:∣
1823:
1809:
1777:β
1755:∣
1739:
1730:
1724:−
1705:∣
1689:
1680:
1592:−
1576:β
1554:∣
1538:
1529:
1523:−
1504:∣
1488:
1479:
1440:β
1434:α
1412:∣
1396:
1387:
1351:β
1345:α
1323:∣
1307:
1298:
1174:β
1168:α
1151:∣
1142:
1133:
1064:θ
965:θ
944:∣
935:
887:θ
864:α
844:β
824:θ
757:θ
738:∣
729:
720:
682:∈
678:β
652:∈
649:α
613:β
605:α
588:∣
579:
570:
528:∈
471:logarithm
215:Segmented
7765:Logistic
7755:Binomial
7734:Isotonic
7729:Quantile
7421:Category
7114:Survival
6991:Johansen
6714:Binomial
6669:Isotonic
6256:(normal)
5901:location
5708:Blocking
5663:Sampling
5542:QâQ plot
5507:Box plot
5489:Graphics
5384:Skewness
5374:Kurtosis
5346:Variance
5276:Heronian
5271:Harmonic
5035:(2000).
4965:10883925
4914:(5): 5.
4883:18051645
4602:See also
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4066:variance
4011:exposure
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2996:′
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2695:vectors
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2327:′
969:′
761:′
617:′
449:form of
330:Bayesian
268:Weighted
263:Ordinary
195:Isotonic
190:Quantile
7760:Poisson
7447:Commons
7394:Kriging
7279:Process
7236:studies
7095:Wavelet
6928:General
6095:Plug-in
5889:L space
5668:Cluster
5369:Moments
5187:Outline
5025:1633357
4863:Bibcode
4855:Ecology
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4677:2347125
4389:of the
4385:is the
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2772:values
473:of its
289:Partial
128:Poisson
7724:Robust
7316:Census
6906:Normal
6854:Manova
6674:Robust
6424:2-way
6416:1-way
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465:has a
247:Linear
185:Robust
108:Probit
34:Models
6940:Trend
6469:prior
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6300:-test
6274:-test
6266:-test
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5911:shape
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445:is a
294:Total
210:Local
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