Integrative rank-based regression for multi-source high-dimensional data with multi-type responses

Practical scenarios often present instances where the types of responses are different between multi-source different datasets, reflecting distinct attributes or characteristics. In this paper, an integrative rank-based regression is proposed to facilitate information sharing among varied datasets with multi-type responses. Taking advantage of the rank-based regression, our proposed approach adeptly tackles differences in the magnitude of loss functions. In addition, it can robustly handle outliers and data contamination, and effectively mitigate model misspecification. Extensive numerical simulations demonstrate the superior and competitive performance of the proposed approach in model estimation and variable selection. Analysis of genetic data on HNSC and LUAD yields results with biological explanations and confirms its practical usefulness.