Variable Selection with Log-Density in Logistic Regression Model

Variable Selection for Multivariate Logistic Regression Models

In this paper, we use multivariate logistic regression models to incorporate correlation among binary response data. Our objective is to develop a variable subset selection procedure to identify important covariates in predicting correlated binary responses using a Bayesian approach. In order to incorporate available prior information, we propose a class of informative prior distributions on th...

Variable Selection in Logistic Regression: The British English Dative Alternation

In this paper, we address the problem of selecting the ‘optimal’ variable subset in a logistic regression model for a medium-sized data set. As a case study, we take the British English dative alternation, where speakers and writers can choose between two (equally grammatical) syntactic constructions to express the same meaning. With the help of 29 explanatory variables taken from the literatur...

Bayesian model selection for logistic regression models with random intercept

Data, collected to model risk of an interesting event, often have a multilevel structure as patients are clustered within larger units, e.g. clinical centers. Risk of the event is usually modeled using a logistic regression model, with a random intercept to control for heterogeneity among clusters. Model specification requires to decide which regressors have a non-negligible effect, and hence, ...

Volumes of logistic regression models with applications to model selection

Logistic regression models with n observations and q linearly-independent covariates are shown to have Fisher information volumes which are bounded below by π and above by ( n q ) π. This is proved with a novel generalization of the classical theorems of Pythagoras and de Gua, which is of independent interest. The finding that the volume is always finite is new, and it implies that the volume c...

Log - Linear Modeling and Logistic Regression