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

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

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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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteNonparametric Statistics 4th ISNPS, Salerno, Italy, June 2018
Numero di pagine10
Stato di pubblicazionePublished - 2020

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