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Introduction to Generalized Linear Mixed Models
Chapter 15 investigated linear mixed-effects models (LMMs). This chapter introduces generalized linear mixed...
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Linear hypothesis testing in ultra high dimensional generalized linear mixed models
This paper is concerned with linear hypothesis testing problems in ultra high dimensional generalized linear mixed models where the response and the...
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Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees
Growth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects...
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Generalized Linear Mixed Models with Applications in Agriculture and Biology
This open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached...
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Selection of Fixed Effects in High-dimensional Generalized Linear Mixed Models
The selection of fixed effects is studied in high-dimensional generalized linear mixed models (HDGLMMs) without parametric distributional assumptions...
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Generalized Linear Mixed Models for Longitudinal Microbiome Data
Chapter 16 investigated some general topics of generalized linear mixed-effects models (GLMMs). This chapter...
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Generalized Linear Models
In the generalized linear model (GLM) (which is not highly general) y = Xβ + ϵ, the response variables are normally distributed, with constant...
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Optimal designs for generalized linear mixed models based on the penalized quasi-likelihood method
While generalized linear mixed models are useful, optimal design questions for such models are challenging due to complexity of the information...
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Elements of Generalized Linear Mixed Models
Linear models are commonly used to describe and analyze datasets from different research areas, such as biological, agricultural, social, and so on....
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Generalized Linear Mixed Models for Non-normal Responses
Generalized linear mixed models (GLMMs) have been recognized as one of the major methodological developments in recent years, which is evidenced by...
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On the application of generalized linear mixed models for predicting path loss in LTE networks
To meet the ever-growing demand for higher data rates, accurate channel models are needed for optimal design and deployment of mobile wireless...
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Generalized Linear Mixed Models for Counts
Data in the for of counts regularly appear in studies in which the number of occurrences is investigated, such as the number of insects, birds, or...
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BG2: Bayesian variable selection in generalized linear mixed models with nonlocal priors for non-Gaussian GWAS data
BackgroundGenome-wide association studies (GWASes) aim to identify single nucleotide polymorphisms (SNPs) associated with a given phenotype. A common...
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Post-Model-Selection Prediction Intervals for Generalized Linear Models
We give two prediction intervals for Generalized Linear Models that take model selection uncertainty into account. The first is a straightforward...
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Generalized Linear Models
This chapter discusses state-of-the-art statistical modeling in insurance and actuarial science, which is the generalized linear model (GLM). We...
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Generalized Linear Mixed Models for Proportions and Percentages
In this chapter, we will review generalized linear mixed models (GLMMs) whose response can be either a proportion or a percentage. For proportion and...
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A Hamiltonian Monte Carlo EM algorithm for generalized linear mixed models with spatial skew latent variables
Spatial generalized linear mixed models with skew latent variables are usually used to model discrete spatial responses that have some skewness....
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Linear Mixed Models
The linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM...
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Generalized Linear Mixed Models for Repeated Measurements
Repeated measures data, also known as longitudinal data, are those derived from experiments in which observations are made on the same experimental...
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Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models
Various privacy-preserving frameworks that respect the individual’s privacy in the analysis of data have been developed in recent years. However,...
