Consider stratified data in which Yi 1, ..., Yi ni denote real-valued response variables corresponding to the observations from stratum i, i = 1, ..., m and suppose that Yi j follows an exponential family distribution with canonical parameter of the form θi j = xi j β + γi. In analyzing data of this type, the stratum-specific parameters are often modeled as random effects; a commonly-used approach is to assume that γ1, ..., γm are independent, identically distributed random variables. The purpose of this paper is to consider an alternative approach to defining the random effects, in which the stratum means of the response variable are assumed to be independent and identically distributed, with a distribution not depending on β. It will be s...
Abstract A generalized linear mixed model with a nonparametric distribution for the random effect is...
We consider the situation where the random effects in a generalized linear mixed model may be correl...
In situations where a large data set is partitioned into many relativelysmall clusters, and where th...
Consider stratified data in which Yi 1, ..., Yi ni denote real-valued response variables correspondi...
The Generalized Linear Mixed Model (GLMM) is a natural extension and mixture of a Linear Mixed Model...
Generalized additive mixed models extend the common parametric predictor of generalized linear model...
Summary. The relationship between a primary endpoint and features of longitudinal profiles of a cont...
We propose Bayesian generalized additive mixed models for correlated data, which arise frequently in...
International audienceIn this paper, an alternative estimation approach is proposed to fit linear mi...
International audienceAn alternative estimation approach is proposed to fit a linear mixed effects m...
xthybrid estimates generalized linear mixed models that split the effects of cluster-varying covaria...
A standard assumption is that the random effects of Generalized Linear Mixed Effects Models (GLMMs) ...
This dissertation considers the problem of learning the underlying statistical structure of complex ...
Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious...
Linear mixed models are a powerful inferential tool in modern statistics and have a wide range of ap...
Abstract A generalized linear mixed model with a nonparametric distribution for the random effect is...
We consider the situation where the random effects in a generalized linear mixed model may be correl...
In situations where a large data set is partitioned into many relativelysmall clusters, and where th...
Consider stratified data in which Yi 1, ..., Yi ni denote real-valued response variables correspondi...
The Generalized Linear Mixed Model (GLMM) is a natural extension and mixture of a Linear Mixed Model...
Generalized additive mixed models extend the common parametric predictor of generalized linear model...
Summary. The relationship between a primary endpoint and features of longitudinal profiles of a cont...
We propose Bayesian generalized additive mixed models for correlated data, which arise frequently in...
International audienceIn this paper, an alternative estimation approach is proposed to fit linear mi...
International audienceAn alternative estimation approach is proposed to fit a linear mixed effects m...
xthybrid estimates generalized linear mixed models that split the effects of cluster-varying covaria...
A standard assumption is that the random effects of Generalized Linear Mixed Effects Models (GLMMs) ...
This dissertation considers the problem of learning the underlying statistical structure of complex ...
Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious...
Linear mixed models are a powerful inferential tool in modern statistics and have a wide range of ap...
Abstract A generalized linear mixed model with a nonparametric distribution for the random effect is...
We consider the situation where the random effects in a generalized linear mixed model may be correl...
In situations where a large data set is partitioned into many relativelysmall clusters, and where th...