Topic
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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TL;DR: Some criteria for measuring the overall precision of a genetic evaluation using linear mixed-model methodology are presented via an extension of the coefficient of determination to linear combinations of estimates and via the use of the Kullback information.
Abstract: Some criteria for measuring the overall precision of a genetic evaluation using linear mixed-model methodology are presented. They are derived via an extension of the coefficient of determination to linear combinations of estimates and via the use of the Kullback information. A parallel is drawn between inestimability of fixed-effects contrasts and the zero coefficient of determination for contrasts of random effects. The procedure is illustrated with 2 minor hypothetical examples of genetic evaluation based on an animal model and on a sire model.
94 citations
TL;DR: The argument for the use of situation specific weights to integrate results from such trials is presented and the inclusion of appropriate study specific scores in an appropriate meta-analysis model permits the quantification of the variation between studies based on something tangible as opposed to the random adjustments made by the random effects model to the pooled effect size.
93 citations
TL;DR: A model is proposed to describe the evolution in continuous time of unobserved cognition in the elderly and assess the impact of covariates directly on it, using data from PAQUID, a French prospective cohort study of ageing.
Abstract: Cognition is not directly measurable. It is assessed using psychometric tests, which can be viewed as quantitative measures of cognition with error. The aim of this article is to propose a model to describe the evolution in continuous time of unobserved cognition in the elderly and assess the impact of covariates directly on it. The latent cognitive process is defined using a linear mixed model including a Brownian motion and time-dependent covariates. The observed psychometric tests are considered as the results of parameterized nonlinear transformations of the latent cognitive process at discrete occasions. Estimation of the parameters contained both in the transformations and in the linear mixed model is achieved by maximizing the observed likelihood and graphical methods are performed to assess the goodness of fit of the model. The method is applied to data from PAQUID, a French prospective cohort study of ageing.
93 citations
TL;DR: In this paper, the authors exploit the link between random effects and penalized regression to develop a simple test for a zero effect, which can be used with generalized linear mixed models, including those estimated by penalized quasilikelihood.
Abstract: SUMMARY Testing that random effects are zero is difficult, because the null hypothesis restricts the corresponding variance parameter to the edge of the feasible parameter space. In the context of generalized linear mixed models, this paper exploits the link between random effects and penalized regression to develop a simple test for a zero effect. The idea is to treat the variance components not being tested as fixed at their estimates and then to express the likelihood ratio as a readily computed quadratic form in the predicted values of the random effects. Under the null hypothesis this has the distribution of a weighted sum of squares of independent standardnormalrandomvariables.Thetestcanbeusedwithgeneralizedlinearmixedmodels, including those estimated by penalized quasilikelihood.
93 citations
TL;DR: In this article, the authors make use of longitudinal data for Denmark, taken from the waves 1995-1999 of the European Community Household Panel, and estimate fixed effects ordered logit models using the estimation methods proposed by Ferrer-i-Carbonel and Frijters (2004) and Das and van Soest (1999).
Abstract: A growing literature seeks to explain differences in individuals' self-reported satisfaction with their jobs. The evidence so far has mainly been based on cross-sectional data and when panel data have been used, individual unobserved heterogeneity has been modelled as an ordered probit model with random effects. This article makes use of longitudinal data for Denmark, taken from the waves 1995-1999 of the European Community Household Panel, and estimates fixed effects ordered logit models using the estimation methods proposed by Ferrer-i-Carbonel and Frijters (2004) and Das and van Soest (1999). For comparison and testing purposes a random effects ordered probit is also estimated. Estimations are carried out separately on the samples of men and women for individuals' overall satisfaction with the jobs they hold. We find that using the fixed effects approach (that clearly rejects the random effects specification), considerably reduces the number of key explanatory variables. The impact of central economic factors is the same as in previous studies, though. Moreover, the determinants of job satisfaction differ considerably between the genders, in particular once individual fixed effects are allowed for.
93 citations