6533b7cffe1ef96bd1257c68
RESEARCH PRODUCT
An Extension of the DgLARS Method to High-Dimensional Relative Risk Regression Models
Angelo MineoErnst WitLuigi Augugliarosubject
Clustering high-dimensional dataComputer sciencedgLARS Gene expression data High-dimensional data Relative risk regression models Sparsity · Survival analysisLeast-angle regressionRelative riskStatisticsEstimatorRegression analysisExtension (predicate logic)High dimensionalSettore SECS-S/01 - StatisticaSurvival analysisdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-01 |