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

2025/06/03 by Mohamed Cherifi, Cherifi, Mohamed, Xujia Zhu +5
Computer Science · Mathematics · #62F10 #62H30 #62J12 #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #FOS: Electrical engineering #Methodology (stat.ME) #Signal Processing (eess.SP) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.03445

openalex publication_date 2025/06/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• High-dimensional logistic regression with missing data: Imputation, regularization, and universality
• Logistic regression with missing responses and predictors: a review of existing approaches and a case study
• Robust low-rank covariance matrix estimation with a general pattern of missing values
• A principal components method to impute missing values for mixed data
• mice: Multivariate Imputation by Chained Equations inR
• Convergence of a stochastic approximation version of the EM algorithm

Cited by

• Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches

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