On variable selection in joint modeling of mean and dispersion

  title={On variable selection in joint modeling of mean and dispersion},
  author={Edmilson R. Pinto and Leandro Pereira},
  journal={Brazilian Journal of Probability and Statistics},
The joint modeling of mean and dispersion (JMMD) provides an efficient method to obtain useful models for the mean and dispersion, especially in problems of robust design experiments. However, in the literature on JMMD there are few works dedicated to variable selection and this theme is still a challenge. In this article, we propose a procedure for selecting variables in JMMD, based on hypothesis testing and the quality of the model’s fit. A criterion for checking the goodness of fit is used… 

Figures and Tables from this paper


Variable selection in joint modelling of the mean and variance for hierarchical data
We propose to extend the use of penalized likelihood variable selection to hierarchical generalized linear models (HGLMs) for jointly modelling the mean and variance structures. We assume a two-level
Variable Selection in Joint Mean and Dispersion Models via Double Penalized Likelihood
In this paper, we propose to jointly model the conditional mean and variance components associated with the response in multilevel data. We set a generalized linear mixed model (GLMM) for the mean
Estimation and variable selection for mixture of joint mean and variance models
Abstract Mixture of regression models are one of the most important statistical data analysis tools in a heterogeneous population. Similar to modeling variance parameter in a homogeneous population,
Variable Selection for Joint Mean and Dispersion Models of the Lognormal Distribution
The lognormal distribution is widely used in applications. Variable se- lection is an important issue in all regression analysis and in this paper we investigate simultaneous variable selection in
Model selection criteria in beta regression with varying dispersion
Two new model selection criteria are introduced that explicitly account for varying dispersion and a fast two step model selection scheme is proposed which is considerably more accurate and is computationally less costly than usual joint model selection.
Generalized linear model selection using R2
Variable selection for joint mean and dispersion models of the inverse Gaussian distribution
The choice of distribution is often made on the basis of how well the data appear to be fitted by the distribution. The inverse Gaussian distribution is one of the basic models for describing
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
Adjusted likelihood methods for modelling dispersion in generalized linear models
This paper considers double generalized linear models, which allow the mean and dispersion to be modelled simultaneously in a generalized linear model context. Estimation of the dispersion parameters
Determination of the best significance level in forward stepwise logistic regression
SAS/IML programs were written to provide Monte Carlo simulations to determine the best a level for the χ2 (a) stopping criterion, which increased linearly the number of predictor variables and depended upon the mean of the binary variables in one population and the difference between the means in the two populations.