Linear mixed model (LMM) methodology is a powerful technology to analyze models containing both the fixed and random effects. The model was first proposed to estimate genetic parameters for unbalanced ...

Bayesian variable selection has gained much empirical success recently in a variety of applications when the number K of explanatory variables $(x_{1},\ldots ,x_{K})$ is possibly much larger than the ...

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

Generalized linear mixed models (GLMM) are useful in a variety of applications. With surrogate covariate data, existing methods of inference for GLMM are usually computationally intensive. We propose ...

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

Researchers and students of applied statistics and the social and behavioral sciences will find this book indispensable for understanding both general linear model theory and application. The model is ...

Researchers from many fields can benefit from applied knowledge of general linear models. This class of models includes the t-test (paired and two sample), regression, ANOVA, and ANCOVA. Like all ...

This course is compulsory on the MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available on the MPhil/PhD in Statistics, MSc in Data ...

Defines the least-squares means for the fixed-effects general linear model. The report also discusses the use of least-squares means in lieu of class or subclass arithmetic means with unbalanced ...