Robust inference with incompleteness for logistic regression model

doi:10.1016/j.sigpro.2025.110027

Robust inference with incompleteness for logistic regression model

2025-05-27

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AI

M

Mehdi Cherifi

M

Mohammed Nabil El Korso *

DOI：10.1016/j.sigpro.2025.110027

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Abstract

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Logistic regression models traditionally assume observed covariates. However, practical scenarios often involve missing data and outliers, which pose significant challenges. This short communication presents a new approach to solve these issues by integrating random covariates following a Student t-distribution within the framework of logistic regression. We propose a Robust Stochastic Approximation Expectation-Maximization algorithm suitable for Logistic Regression (REM-LR) that, in addition, is able to improve the resilience of the model against both missing values and outliers.

Keywords:

Parametric estimation

Missing values

Robustness

Maximum likelihood

Expectation-maximization algorithm

Logistic regression

Robust inference with incompleteness for logistic regression model

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