The Dantzig Selector in Cox's Proportional Hazards Model

Anestis Antoniadis, Piotr Fryzlewicz and Frédérique Letué

Scandinavian Journal of Statistics, 2010, vol. 37, issue 4, 531-552

Abstract: Abstract. The Dantzig selector (DS) is a recent approach of estimation in high‐dimensional linear regression models with a large number of explanatory variables and a relatively small number of observations. As in the least absolute shrinkage and selection operator (LASSO), this approach sets certain regression coefficients exactly to zero, thus performing variable selection. However, such a framework, contrary to the LASSO, has never been used in regression models for survival data with censoring. A key motivation of this article is to study the estimation problem for Cox's proportional hazards (PH) function regression models using a framework that extends the theory, the computational advantages and the optimal asymptotic rate properties of the DS to the class of Cox's PH under appropriate sparsity scenarios. We perform a detailed simulation study to compare our approach with other methods and illustrate it on a well‐known microarray gene expression data set for predicting survival from gene expressions.

Date: 2010
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https://doi.org/10.1111/j.1467-9469.2009.00685.x

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