Adjusted regularized estimation in the accelerated failure time model with high dimensional covariates

Jianwei Hu and Hao Chai

Journal of Multivariate Analysis, 2013, vol. 122, issue C, 96-114

Abstract: Based on Stute’s weighted least squares method, we consider the estimate procedures for the accelerated failure time (AFT) model with high dimensional covariates. We use Kaplan–Meier weights and Stute’s estimator to account for censoring in least squares estimation. We consider two regularization approaches, the least absolute shrinkage and selection operator (LASSO) and minimax concave penalty (MCP) for estimation and variable selection in the AFT model. Our work includes the following contributions: (1) we adjust the penalty term in the objective function and show that this adjustment is crucial for the AFT model when the scales of covariates are heterogeneous; (2) with this adjustment, we also get a desired property for LASSO estimator in the AFT model, just like the uncensored case; (3) we replace a key assumption required in the high dimensional AFT model with a set of conditions which essentially require the fraction of censored data to be small; (4) we get selection consistency of MCP in the AFT model.

Keywords: Accelerated failure time model; High-dimensional data; Least absolute shrinkage and selection operator; Minimax concave penalty (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X13001437
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:122:y:2013:i:c:p:96-114

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2013.07.011

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().