A1604
Title: Robust joint modeling for data with continuous and binary responses
Authors: Lulu Kang - University of Massachusetts Amherst (United States) [presenting]
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. However, outliers pose a new challenge to existing likelihood-based modeling approaches. A new robust joint modeling framework for data with both continuous and binary responses is proposed based on the density power divergence (DPD) loss function with l1 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. An efficient proximal gradient algorithm with Barzilai-Borwein spectral step size and a robust information criterion (RIC) for data-driven selection of penalty parameters are developed. 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.
