Predictive Subgroup Logistic Regression for Classification with Unobserved Heterogeneity

Kun Chen; Rui Huang; Zhiwei Tong

doi:10.1007/s11222-025-10712-9

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Predictive Subgroup Logistic Regression for Classification with Unobserved Heterogeneity

Journal article

Peer reviewed

Predictive Subgroup Logistic Regression for Classification with Unobserved Heterogeneity

Kun Chen, Rui Huang and Zhiwei Tong

Statistics and computing, Vol.35(6), 185

09/05/2025

DOI: 10.1007/s11222-025-10712-9

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Abstract

Unobserved heterogeneity refers to the variation among subjects that is not accounted for by the observed features used in a model. Its presence poses a substantial challenge to statistical modeling. This study introduces the Predictive Subgroup Logistic Regression (PSLR) model, which extends the conventional logistic regression and is specifically designed to address unobserved heterogeneity in classification problems. The PSLR model incorporates subject-specific intercepts in the log odds, fitted through a penalized likelihood approach with a concave pairwise fusion penalty. A novel two-step procedure is developed to facilitate the out-of-sample predictions for new subjects whose subgroup membership labels are unknown. This procedure allows the PSLR model to perform both inferential and predictive tasks. Through extensive simulation studies and an empirical application to a customer churn dataset in the telecommunications industry, the PSLR model not only demonstrates great performance in various aggregate accuracy metrics but also achieves a balanced effectiveness in sensitivity and specificity.

Computer Science

Artificial Intelligence

Original Paper

Probability and Statistics in Computer Science

Statistical Theory and Methods

Statistics and Computing/Statistics Programs

Details

Title: Subtitle: Predictive Subgroup Logistic Regression for Classification with Unobserved Heterogeneity
Creators: Kun Chen - Southwestern University of Finance and Economics
Rui Huang - Nanjing University of Chinese Medicine
Zhiwei Tong - University of Iowa
Resource Type: Journal article
Publication Details: Statistics and computing, Vol.35(6), 185
DOI: 10.1007/s11222-025-10712-9
ISSN: 0960-3174
eISSN: 1573-1375
Publisher: Springer US
Grant note: 24BTJ071 / National Social Science Fund of China (https://doi.org/10.13039/501100012456) 72203090 / National Natural Science Foundation of China (https://doi.org/10.13039/501100001809)
Language: English
Date published: 09/05/2025
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9984958286802771

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