Add to ORKGAbstract: Random-effect models are becoming increasingly popular in the analysis of data. Lee and Nelder (2006) introduced double hierarchical generalized linear models (DHGLMs) in which not only the mean but also the residual variance (overdispersion) can be further modelled as random-effect models. In this article, we introduce DHGLMs that allow random-effect models for both the variances of random effects and the residual variance. We show how to use this general model class for the analysis of data and discuss how to select the best fitting model using the likelihood and various model-checking plots. Key words: Double hierarchical generalized linear models; hierarchical generalized linear models; hierarchical likelihood; random effect
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
Reinforcement learning is one of the paradigms and methodologies of machine learning developed in th...
We propose a class of double hierarchical generalized linear models in which random effects can be s...
The R package HGLMMM has been developed to fit generalized linear models with random effects using t...
Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the general...
The R package HGLMMM has been developed to fit generalized linear models with random effects using t...
We present the hglm package for fitting hierarchical generalized linear models. It can be used for l...
Hierarchical generalized linear models are often used to fit random effects models. However, attenti...
Hierarchical generalized linear models are often used to fit random effects models. However, attenti...
Abstract: Generalized Linear Models (GLMs) are a popular class of regression models when the respons...
Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious...
In random effect models, error variance (stage 1 variance) and scalar random effect variance compone...
An approach for developing Bayesian outlier and goodness of fit statistics is presented for the line...
The current work deals with modelling longitudinal or repeated non-Gaussian measurements for a respi...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
Reinforcement learning is one of the paradigms and methodologies of machine learning developed in th...
We propose a class of double hierarchical generalized linear models in which random effects can be s...
The R package HGLMMM has been developed to fit generalized linear models with random effects using t...
Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the general...
The R package HGLMMM has been developed to fit generalized linear models with random effects using t...
We present the hglm package for fitting hierarchical generalized linear models. It can be used for l...
Hierarchical generalized linear models are often used to fit random effects models. However, attenti...
Hierarchical generalized linear models are often used to fit random effects models. However, attenti...
Abstract: Generalized Linear Models (GLMs) are a popular class of regression models when the respons...
Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious...
In random effect models, error variance (stage 1 variance) and scalar random effect variance compone...
An approach for developing Bayesian outlier and goodness of fit statistics is presented for the line...
The current work deals with modelling longitudinal or repeated non-Gaussian measurements for a respi...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
Reinforcement learning is one of the paradigms and methodologies of machine learning developed in th...