A novel multivariate regression model for unbalanced binary data : a strong conjugacy under random effect approach

TL;DR

This paper introduces a new multivariate regression model for correlated binary data using a generalized log-gamma random effect, providing an effective approach for unbalanced and balanced datasets with strong conjugacy properties.

Contribution

The paper develops the MBerGLG regression model with strong conjugacy, deriving a new multivariate distribution for correlated binary data with efficient estimation methods.

Findings

Maximum likelihood estimators are unbiased and efficient.

The model effectively fits both unbalanced and balanced binary data.

Residual analysis detects model departures and outliers.

Abstract

In this paper, we deduce a new multivariate regression model designed to fit correlated binary data. The multivariate distribution is derived from a Bernoulli mixed model with a nonnormal random intercept on the marginal approach. The random effect distribution is assumed to be the generalized log-gamma (GLG) distribution by considering a particular parameter setting. The complement log-log function is specified to lead to strong conjugacy between the response variable and random effect. The new discrete multivariate distribution, named MBerGLG distribution, has location and dispersion parameters. The MBerGLG distribution leads to the MBerGLG regression (MBerGLGR) model, providing an alternative approach to fitting both unbalanced and balanced correlated response binary data. Monte Carlo simulation studies show that its maximum likelihood estimators are unbiased, efficient, and consistent asymptotically. The randomized quantile residuals are performed to identify possible departures from the proposal model and the data and detect atypical subjects. Finally, two applications are presented in the data analysis section.