• Corpus ID: 248987510

Multivariate generalized linear mixed models for underdispersed count data

  title={Multivariate generalized linear mixed models for underdispersed count data},
  author={Guilherme Parreira da Silva and Henrique Aparecido Laureano and Ricardo Rasmussen Petterle and Paulo Justiniano Ribeiro J'unior and Wagner Hugo Bonat},
Researchers are often interested in understanding the relationship between a set of covariates and a set of response variables. To achieve this goal, the use of regression analysis, either linear or generalized linear models, is largely applied. However, such models only allow users to model one response variable at a time. Moreover, it is not possible to directly calculate from the regression model a correlation measure between the response variables. In this article, we employed the… 

Figures and Tables from this paper


MCMC methods for multi-response generalized linear mixed models
The R package MCMCglmm implements Markov chain Monte Carlo methods for generalized linear mixed models, which provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form.
Multivariate covariance generalized linear models
We propose a general framework for non‐normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a
Extended Poisson–Tweedie: Properties and regression models for count data
We propose a new class of discrete generalized linear models based on the class of Poisson–Tweedie factorial dispersion models with variance of the form μ + ϕ μ p , where μ is the mean and ϕ and p
Mean-parametrized Conway–Maxwell–Poisson regression models for dispersed counts
Conway–Maxwell–Poisson (CMP) distributions are flexible generalizations of the Poisson distribution for modelling overdispersed or underdispersed counts. The main hindrance to their wider use in
Fitting Linear Mixed-Effects Models Using lme4
Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most
It is shown how the concept of a random coefficient model can be extended to nonlinear models so as to fit nonlinear mixed-effects models, and how this can be used in a variety of situations.
Approximate inference in generalized linear mixed models
Statistical approaches to overdispersion, correlated errors, shrinkage estimation, and smoothing of regression relationships may be encompassed within the framework of the generalized linear mixed
Regression Models for Count Data in R
The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in
Hierarchical Generalized Linear Models
We consider hierarchical generalized linear models which allow extra error components in the linear predictors of generalized linear models. The distribution of these components is not restricted to
A statistical model for under‐ or overdispersed clustered and longitudinal count data
Application of the likelihood‐based model to daily counts of asthma inhaler use by children shows substantial within‐subject underdispersion, between‐subject heterogeneity and correlation due to both clustering of measurements within subjects and serial correlation of longitudinal measurements.