ArticleJournal of applied statistics2025
Integrative rank-based regression for multi-source high-dimensional data with multi-type responses.
Article in Journal of applied statistics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
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.
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