An Extension of the DgLARS Method to High-Dimensional Relative Risk Regression Models

Angelo Mineo, Luigi Augugliaro, Luigi Augugliaro, Angelo M. Mineo, Ernst C. Wit, Ernst Jancamiel Wit

Research output: Chapter in Book/Report/Conference proceedingChapter


In recent years, clinical studies, where patients are routinely screened for many genomic features, are becoming more common. The general aim of such studies is to find genomic signatures useful for treatment decisions and the development of new treatments. However, genomic data are typically noisy and high dimensional, not rarely outstripping the number of patients included in the study. For this reason, sparse estimators are usually used in the study of high-dimensional survival data. In this paper, we propose an extension of the differential geometric least angle regression method to high-dimensional relative risk regression models.
Original languageEnglish
Title of host publicationNonparametric Statistics 4th ISNPS, Salerno, Italy, June 2018
Number of pages10
Publication statusPublished - 2020

Publication series


All Science Journal Classification (ASJC) codes

  • General Mathematics

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