Results 251 to 260 of about 18,973,572 (296)
Some of the next articles are maybe not open access.
Generalized quasi-linear mixed-effects model
Statistical Methods in Medical Research, 2022The generalized linear mixed model (GLMM) is one of the most common method in the analysis of longitudinal and clustered data in biological sciences. However, issues of model complexity and misspecification can occur when applying the GLMM. To address these issues, we extend the standard GLMM to a nonlinear mixed-effects model based on quasi-linear ...
Yusuke Saigusa +2 more
openaire +2 more sources
Deletion measures for generalized linear mixed effects models
Computational Statistics & Data Analysis, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liang Xu, Sik-Yum Lee, Wai-Yin Poon
openaire +3 more sources
Empirical model selection in generalized linear mixed effects models
Computational Statistics, 2007This paper focuses on model selection in generalized linear mixed models using an information criterion approach. In these models in general, the response marginal distribution cannot be analytically derived. Thus, for parameter estimation, two approximations are revisited both leading to iterative model linearizations.
Christian Lavergne +2 more
openaire +2 more sources
Interval Estimation of the Intra-class Correlation in General Linear Mixed Effects Models
Journal of Statistical Theory and Practice, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiaoshu Feng +2 more
openaire +1 more source
Generalized Multi-linear Mixed Effects Model
2016Recently, many applications tend to find common and distinctive features from a group of datasets, of which distributions and structures are generally various. However, most existing methods can just cope with specific problems with fixed distributions and structures.
Chao Li 0013 +4 more
openaire +2 more sources
Journal of the American Statistical Association, 2016
ABSTRACTConsidering model averaging estimation in generalized linear models, we propose a weight choice criterion based on the Kullback–Leibler (KL) loss with a penalty term. This criterion is different from that for continuous observations in principle, but reduces to the Mallows criterion in the situation.
Xinyu Zhang +3 more
openaire +1 more source
ABSTRACTConsidering model averaging estimation in generalized linear models, we propose a weight choice criterion based on the Kullback–Leibler (KL) loss with a penalty term. This criterion is different from that for continuous observations in principle, but reduces to the Mallows criterion in the situation.
Xinyu Zhang +3 more
openaire +1 more source
Module M.7 Generalized linear mixed-effects models
2023Introduction to Primate Data Exploration and Linear Modeling with R was created with the goal of providing training to undergraduate biology students on data management and statistical analysis using authentic data of Cayo Santiago rhesus macaques. Module M.7 introduces generalized linear mixed-effects modeling in R.
Bland, Alexandra +1 more
openaire +1 more source
Generalized S-estimators for linear mixed effects models
Statistica Sinica, 2014Linear mixed effects (LME) models are important statistical tools for analysis of clustered and correlated data. High breakdown estimators are currently the robust methods of choice for multivariate linear regression, but extensions of such estimators have been developed only for completely balanced LME models.
Inna Chervoneva, Mark Vishnyakov
openaire +1 more source
Efficient estimation of general linear mixed effects models
Journal of Statistical Planning and Inference, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Demidenko, Eugene, Stukel, Thérèse A.
openaire +2 more sources