Robust Joint Modeling for Data with Continuous and Binary Responses

TL;DR

This paper introduces a robust joint modeling framework for mixed continuous and binary data that improves prediction accuracy and robustness against outliers using density power divergence and regularization.

Contribution

It proposes a novel robust joint modeling approach with a new loss function, regularization, and an efficient algorithm for high-dimensional mixed response data.

Findings

Outperforms existing methods in simulation studies with contamination.

Achieves lower prediction error and more accurate parameter estimates.

Demonstrates practical benefits in semiconductor manufacturing data.

Abstract

In many supervised learning applications, the response consists of both continuous and binary outcomes. Studies have shown that jointly modeling such mixed-type responses can substantially improve predictive performance compared to separate analyses. But outliers pose a new challenge to the existing likelihood-based modeling approaches. In this paper, we propose a new robust joint modeling framework for data with both continuous and binary responses. It is based on the density power divergence (DPD) loss function with the $\ell_1$ regularization. The proposed framework leads to a sparse estimator that simultaneously predicts continuous and binary responses in high-dimensional input settings while down-weighting influential outliers and mislabeled samples. We also develop an efficient proximal gradient algorithm with Barzilai-Borwein spectral step size and a robust information criterion (RIC) for data-driven selection of the penalty parameters. Extensive simulation studies under a variety of contamination schemes demonstrate that the proposed method achieves lower prediction error and more accurate parameter estimation than several competing approaches. A real case study on wafer lapping in semiconductor manufacturing further illustrates the practical gains in predictive accuracy, robustness, and interpretability of the proposed framework.