scholarly journals Applying Linear Mixed Effects Models with Crossed Random Effects to Psycholinguistic Data: Multilevel Specification and Model Selection.

2015 ◽  
Vol 11 (2) ◽  
pp. 78-88 ◽  
Author(s):  
Hsiu-Ting Yu
Keyword(s):  
Model Selection ◽  
Random Effects ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Crossed Random Effects

Empirical model selection in generalized linear mixed effects models

2007 ◽  
Vol 23 (1) ◽  
pp. 99-109 ◽  
Author(s):  
Christian Lavergne ◽  
Marie-José Martinez ◽  
Catherine Trottier
Keyword(s):  
Model Selection ◽  
Empirical Model ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Generalized Linear Mixed Effects

scholarly journals Permutation and Bayesian tests for testing random effects in linear mixed‐effects models

2019 ◽  
Vol 38 (25) ◽  
pp. 5034-5047 ◽  
Author(s):  
Kaidi Rao ◽  
Reza Drikvandi ◽  
Benjamin Saville
Keyword(s):  
Random Effects ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects

scholarly journals Computationally Stable Estimation Procedure for the Multivariate Linear Mixed-Effect Model and Application to Malaria Public Health Problem

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Eric Houngla Adjakossa ◽  
Norbert Mahouton Hounkonnou ◽  
Grégory Nuel
Keyword(s):  
Em Algorithm ◽  
Covariance Matrix ◽  
Random Effects ◽  
Estimation Procedure ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Consistent Estimate ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
The Em Algorithm

Abstract In this paper, we provide the ML (Maximum Likelihood) and the REML (REstricted ML) criteria for consistently estimating multivariate linear mixed-effects models with arbitrary correlation structure between the random effects across dimensions, but independent (and possibly heteroscedastic) residuals. By factorizing the random effects covariance matrix, we provide an explicit expression of the profiled deviance through a reparameterization of the model. This strategy can be viewed as the generalization of the estimation procedure used by Douglas Bates and his co-authors in the context of the fitting of one-dimensional linear mixed-effects models. Beside its robustness regarding the starting points, the approach enables a numerically consistent estimate of the random effects covariance matrix while classical alternatives such as the EM algorithm are usually non-consistent. In a simulation study, we compare the estimates obtained from the present method with the EM algorithm-based estimates. We finally apply the method to a study of an immune response to Malaria in Benin.

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