Robust mislabel logistic regression without modeling mislabel probabilities

TL;DR

This paper introduces a robust mislabel logistic regression method based on gamma-divergence that does not require modeling mislabel probabilities, offering automatic bias correction and improved robustness.

Contribution

It proposes a novel gamma-divergence based approach for mislabel logistic regression that simplifies implementation and enhances robustness without modeling mislabel probabilities.

Findings

Demonstrates superior robustness in simulations

Shows effective performance on real data

Provides easy-to-implement algorithms

Abstract

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on gamma-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum gamma-divergence estimation leads to a weighted estimating equation without the need to subtract any bias correction term, i.e., it is automatically bias corrected. These properties make the proposed gamma-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation and real data application results are presented to demonstrate the performance of gamma-logistic.