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Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.


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01 Nov 2011
TL;DR: This paper presents a meta-modelling architecture for binary regression that combines log-linear and graphical models, and some basic tools for random effects modeling are presented.
Abstract: 1. Introduction 2. Binary regression: the logit model 3. Generalized linear models 4. Modeling of binary data 5. Alternative binary regression models 6. Regularization and variable selection for parametric models 7. Regression analysis of count data 8. Multinomial response models 9. Ordinal response models 10. Semi- and nonparametric generalized regression 11. Tree-based methods 12. The analysis of contingency tables: log-linear and graphical models 13. Multivariate response models 14. Random effects models 15. Prediction and classification Appendix A. Distributions Appendix B. Some basic tools Appendix C. Constrained estimation Appendix D. Kullback-Leibler distance and information-based criteria of model fit Appendix E. Numerical integration and tools for random effects modeling.

253 citations

Journal ArticleDOI
TL;DR: In this article, an efficient algorithm for computing restricted maximum likelihood estimates of variance components in a class of models is described, characterized by effects to be absorbed, which are nested within herds, other fixed effects, random sire effects, and a random residual.

250 citations

Journal ArticleDOI
TL;DR: In this paper, a score test for autocorrelation in the within-individual errors for the conditional independence random effects model was developed and an explicit maximum likelihood estimation procedure using the scoring method for the model with random effects and AR(1) errors was derived.
Abstract: For longitudinal data on several individuals, linear models that contain both random effects across individuals and autocorrelation in the within-individual errors are studied. A score test for autocorrelation in the within-individual errors for the “conditional independence” random effects model is first developed. An explicit maximum likelihood estimation procedure using the scoring method for the model with random effects and (autoregressive) AR(1) errors is then derived. Empirical Bayes estimation of the random effects and prediction of future responses of an individual based on this random effects with AR(1) errors model are also considered. A numerical example is presented to illustrate these methods.

250 citations

Journal ArticleDOI
TL;DR: The authors showed that the marginal AIC is not an asymptotically unbiased estimator of the Akaike information, and that ignoring estimation uncertainty in the random effects covariance matrix induces a bias that can lead to the selection of any random effect not predicted to be exactly zero.
Abstract: In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion, AIC, have been used, based either on the marginal or on the conditional distribution. We show that the marginal AIC is not an asymptotically unbiased estimator of the Akaike information, and favours smaller models without random effects. For the conditional AIC, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that can lead to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional AIC, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in an R package (R Development Core Team, 2010) is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.

249 citations

Journal ArticleDOI
TL;DR: MIXREG is a program that provides estimates for a mixed-effects regression model (MRM) for normally-distributed response data including autocorrelated errors, utilizing both the EM algorithm and a Fisher-scoring solution.

249 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023198
2022433
2021409
2020380
2019404