Table of Contents
Journal of Quality and Reliability Engineering
Volume 2015, Article ID 795154, 9 pages
Research Article

Variable Selection Methods for Right-Censored Time-to-Event Data with High-Dimensional Covariates

Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA

Received 1 October 2014; Revised 13 April 2015; Accepted 16 April 2015

Academic Editor: Christian Kirchsteiger

Copyright © 2015 Keivan Sadeghzadeh and Nasser Fard. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Advancement in technology has led to greater accessibility of massive and complex data in many fields such as quality and reliability. The proper management and utilization of valuable data could significantly increase knowledge and reduce cost by preventive actions, whereas erroneous and misinterpreted data could lead to poor inference and decision making. On the other side, it has become more difficult to process the streaming high-dimensional time-to-event data in traditional application approaches, specifically in the presence of censored observations. This paper presents a multipurpose analytic model and practical nonparametric methods to analyze right-censored time-to-event data with high-dimensional covariates. In order to reduce redundant information and to facilitate practical interpretation, variable inefficiency in failure time is determined for the specific field of application. To investigate the performance of the proposed methods, these methods are compared with recent relevant approaches through numerical experiments and simulations.