TY - CHAP

T1 - An Extension of the DgLARS Method to High-Dimensional Relative Risk Regression Models

AU - Augugliaro, Luigi

AU - Mineo, Angelo

AU - Augugliaro, Luigi

AU - Mineo, Angelo M.

AU - Wit, Ernst C.

AU - Wit, Ernst Jancamiel

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10447/452683

M3 - Chapter

SN - 978-3-030-57305-8; 978-3-030-57306-5

T3 - SPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS

SP - 57

EP - 66

BT - Nonparametric Statistics 4th ISNPS, Salerno, Italy, June 2018

ER -