"First edition published in 2006." 1. Introduction -- What are linear mixed models (LMMs)? -- Models with random effects for clustered data -- Models for longitudinal or repeated-measures data -- A ...

Keywords: Statistical analyses. Regression models. Post-earthquake ignitions. Data analyses. California. Ground shaking. Generalized linear mixed models. Goodness-of ...

In this article, the authors suggest a profile-likelihood approach for estimating complex models by maximum likelihood (ML) using standard software and minimal programming. The method works whenever ...

Limitations of linear regression applied on ecological data -- Things are not always linear : additive modeling -- Dealing with heterogeneity -- Mixed effects modeling for nested data -- Violation of ...

This section provides an overview of a likelihood-based approach to general linear mixed models. This approach simplifies and unifies many common statistical analyses, including those involving ...

Dirichlet process (DP) priors are a popular choice for semiparametric Bayesian random effect models. The fact that the DP prior implies a non-zero mean for the random effect distribution creates an ...