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

arXiv: https://arxiv.org/abs/2506.03445 · HTML: https://mlsanity.com/paper/2506.03445/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [Amortized Variational Inference for Logistic Regression with Missing Covariates](https://mlsanity.com/paper/2603.21244/) (2026)

## Abstract (arXiv)

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.