Results 161 to 170 of about 4,106,343 (229)

Local Influence in Linear Mixed Models

Biometrics, 1998
The linear mixed model has become an important tool in modelling, partially due to the introduction of the SAS procedure MIXED, which made the method widely available to practising statisticians. Its growing popularity calls for data-analytic methods to check the underlying assumptions and robustness. Here, the problem of detecting influential subjects
Lesaffre, Emmanuel, Verbeke, Geert
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Linear Mixed Models

2012
Fixed effects and random effects models are introduced with examples. Maximum likelihood (ML), restricted maximum likelihood (REML) and ANOVA methods of estimation of variance components are described, illustrating with the examples of one-way and two-way classification.
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Model comparison of generalized linear mixed models

Statistics in Medicine, 2005
AbstractGeneralized 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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Linear Mixed Models

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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Linear Mixed Model

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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Linear and generalized linear mixed models

2015
AbstractGeneralized 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

2017
For 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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Generalized linear mixed models: a practical guide for ecology and evolution.

Trends in Ecology & Evolution, 2009
Benjamin M. Bolker, Mollie E. Brooks, C. Clark, S. Geange, J. Poulsen, M. Stevens, Jada White   +6 more
semanticscholar   +1 more source

Molecular Design Strategies toward Improvement of Charge Injection and Ionic Conduction in Organic Mixed Ionic–Electronic Conductors for Organic Electrochemical Transistors

Chemical Reviews, 2022
Nadzeya A Kukhta, Adam Marks, Christine K Luscombe   +2 more
exaly  

Speed limit reduction in urban areas: a before-after study using Bayesian generalized mixed linear models.

Accident Analysis and Prevention, 2014
S. Heydari, L. Miranda-Moreno, Liping Fu
semanticscholar   +1 more source