Flexible modelling of random effects in linear mixed models—A Bayesian approach

2008 ◽  
Vol 52 (3) ◽  
pp. 1347-1361 ◽  
Author(s):  
Remus K.W. Ho ◽  
Inchi Hu
Keyword(s):  
Random Effects ◽  
Bayesian Approach ◽  
Mixed Models ◽  
Linear Mixed Models

Inferences in linear mixed models with skew-normal random effects

2015 ◽  
Vol 31 (4) ◽  
pp. 576-594 ◽  
Author(s):  
Ren Dao Ye ◽  
Tong Hui Wang
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
Skew Normal

scholarly journals Basic Features of the Analysis of Germination Data with Generalized Linear Mixed Models

Data ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 6 ◽  
Author(s):  
Alberto Gianinetti
Keyword(s):  
Boundary Conditions ◽  
Longitudinal Studies ◽  
Random Effects ◽  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Covariance Structure ◽  
Conditional Models ◽  
Error Terms ◽  
Germination Indices

Germination data are discrete and binomial. Although analysis of variance (ANOVA) has long been used for the statistical analysis of these data, generalized linear mixed models (GzLMMs) provide a more consistent theoretical framework. GzLMMs are suitable for final germination percentages (FGP) as well as longitudinal studies of germination time-courses. Germination indices (i.e., single-value parameters summarizing the results of a germination assay by combining the level and rapidity of germination) and other data with a Gaussian error distribution can be analyzed too. There are, however, different kinds of GzLMMs: Conditional (i.e., random effects are modeled as deviations from the general intercept with a specific covariance structure), marginal (i.e., random effects are modeled solely as a variance/covariance structure of the error terms), and quasi-marginal (some random effects are modeled as deviations from the intercept and some are modeled as a covariance structure of the error terms) models can be applied to the same data. It is shown that: (a) For germination data, conditional, marginal, and quasi-marginal GzLMMs tend to converge to a similar inference; (b) conditional models are the first choice for FGP; (c) marginal or quasi-marginal models are more suited for longitudinal studies, although conditional models lead to a congruent inference; (d) in general, common random factors are better dealt with as random intercepts, whereas serial correlation is easier to model in terms of the covariance structure of the error terms; (e) germination indices are not binomial and can be easier to analyze with a marginal model; (f) in boundary conditions (when some means approach 0% or 100%), conditional models with an integral approximation of true likelihood are more appropriate; in non-boundary conditions, (g) germination data can be fitted with default pseudo-likelihood estimation techniques, on the basis of the SAS-based code templates provided here; (h) GzLMMs are remarkably good for the analysis of germination data except if some means are 0% or 100%. In this case, alternative statistical approaches may be used, such as survival analysis or linear mixed models (LMMs) with transformed data, unless an ad hoc data adjustment in estimates of limit means is considered, either experimentally or computationally. This review is intended as a basic tutorial for the application of GzLMMs, and is, therefore, of interest primarily to researchers in the agricultural sciences.

Testing for random effects in linear mixed models for longitudinal data under moment conditions

2010 ◽  
Vol 26 (3) ◽  
pp. 497-514 ◽  
Author(s):  
Zai Xing Li ◽  
Li Xing Zhu ◽  
Ping Wu ◽  
Jian Hong Wu ◽  
Wang Li Xu
Keyword(s):  
Longitudinal Data ◽  
Random Effects ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
Moment Conditions

Linear mixed models with skew-elliptical distributions: A Bayesian approach

2008 ◽  
Vol 52 (11) ◽  
pp. 5033-5045 ◽  
Author(s):  
Alejandro Jara ◽  
Fernando Quintana ◽  
Ernesto San Martín
Keyword(s):  
Bayesian Approach ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
Elliptical Distributions
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