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Model comparison of generalized linear mixed models
Statistics in Medicine, 2005AbstractGeneralized linear mixed models (GLMMs) have been widely appreciated in biological and medical research. Maximum likelihood estimation has received a great deal of attention. Comparatively, not much has been done on model comparison or hypotheses testing.
Xin-Yuan, Song, Sik-Yum, Lee
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2009
In the early 1950s, C.R. Henderson developed mixed model estimation, something he began in the 1940s with his Ph.D. thesis. He wanted to analyze data for a linear model with fixed environmental and random genetic factors in the breeding of swine (Van Vleck, 1998).
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In the early 1950s, C.R. Henderson developed mixed model estimation, something he began in the 1940s with his Ph.D. thesis. He wanted to analyze data for a linear model with fixed environmental and random genetic factors in the breeding of swine (Van Vleck, 1998).
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2013
The simplest form of the linear mixed model is the random-effects model, which represents data using the regression equation: $$\displaystyle{ \mathbf{y}_{i} =\boldsymbol{\alpha } +\mathbf{b}_{i} +\boldsymbol{\epsilon } _{i} (1 \leq i \leq m), }$$ where \(\boldsymbol{\alpha }\), y i , b i , and \(\boldsymbol{\epsilon }_{i}\) are column matrices ...
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The simplest form of the linear mixed model is the random-effects model, which represents data using the regression equation: $$\displaystyle{ \mathbf{y}_{i} =\boldsymbol{\alpha } +\mathbf{b}_{i} +\boldsymbol{\epsilon } _{i} (1 \leq i \leq m), }$$ where \(\boldsymbol{\alpha }\), y i , b i , and \(\boldsymbol{\epsilon }_{i}\) are column matrices ...
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Linear and generalized linear mixed models
2015AbstractGeneralized linear mixed models (GLMMs) are a powerful class of statistical models that combine the characteristics of generalized linear models and mixed models (models with both fixed and random predictor variables). This chapter: reviews the conceptual and theoretical background of GLMMs, focusing on the definition and meaning of random ...
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Generalized Linear Mixed Models
2017For analyzing repeated measures data, the necessity of considering the relationships between outcome variables as well as between outcome variables and explanatory variable are of concern. We have discussed about such models in previous chapters. All the models proposed in various chapters are fixed effect models. However, in some cases, the dependence
M. Ataharul Islam, Rafiqul I. Chowdhury
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2011
This chapter expands on linear models through the introduction of random effects, where the independent variables in the model include those variables that are fixed and those that vary across subjects. The general linear mixed effects model (LMEM) is introduced as methods for parameter estimation – maximum likelihood and restricted maximum likelihood.
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This chapter expands on linear models through the introduction of random effects, where the independent variables in the model include those variables that are fixed and those that vary across subjects. The general linear mixed effects model (LMEM) is introduced as methods for parameter estimation – maximum likelihood and restricted maximum likelihood.
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