Maximum Likelihood for Logistic Regression Model With Incomplete and Hybrid-Type Covariates
Year: 2025
Type: article
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.
Source: IEEE Signal Processing Letters
Authors: M. Cherifi, X. Zhu, M. N. El Korso, A. Mesloub
Institution: Université Paris-Saclay
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411639837