A mixture logistic model for panel data with a Markov structure

TL;DR

This paper introduces a mixture logistic regression model with a Markov structure for panel data, along with a maximum likelihood estimation approach and a variable selection algorithm to identify key predictors.

Contribution

The paper presents a novel mixture logistic model with Markov dependence and a forward variable selection method for efficient parameter estimation.

Findings

Effective model for panel data with Markov structure

Successful variable selection reducing model complexity

Potential for improved predictive accuracy

Abstract

In this study, we propose a mixture logistic regression model with a Markov structure, and consider the estimation of model parameters using maximum likelihood estimation. We also provide a forward type variable selection algorithm to choose the important explanatory variables to reduce the number of parameters in the proposed model.