Maximum Likelihood for Logistic Regression Model with Incomplete and Hybrid-Type Covariates

TL;DR

This paper introduces an EM-based algorithm for logistic regression that effectively handles missing data in hybrid covariates, improving estimation accuracy in complex real-world scenarios.

Contribution

It presents a novel EM algorithm specifically designed for logistic regression with incomplete hybrid covariates, enhancing estimation efficiency and accuracy.

Findings

Outperforms traditional methods in simulations

Achieves higher accuracy in real-world data

Provides reliable parameter estimates with missing hybrid data

Abstract

Logistic regression is a fundamental and widely used statistical method for modeling binary outcomes based on covariates. However, the presence of missing data, particularly in settings involving hybrid covariates (a mix of discrete and continuous variables), poses significant challenges. In this paper, we propose a novel Expectation-Maximization based algorithm tailored for parameter estimation in logistic regression models with missing hybrid covariates. The proposed method is specifically designed to handle these complexities, delivering efficient parameter estimates. Through comprehensive simulations and real-world application, we demonstrate that our approach consistently outperforms traditional methods, achieving superior accuracy and reliability.