Variable Selection for Panel Count Data via Nonconcave Penalized Estimating Function

• Activity Number: 458
• Type: Topic Contributed
• Date/Time: Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Xin He*+ and Xingwei Tong and Liuquan Sun and Jianguo Sun
• Companies: University of Maryland and Beijing Normal University and Chinese Academy of Sciences and University of Missouri
• Keywords: estimating function ; non-concave penalized procedure ; oracle procedure ; panel count data ; variable selection

Abstract

Panel count data often occur in long-term studies that concern occurrence rate of a recurrent event, and their analysis has recently attracted a great deal of attention. However, there does not seem to exist any established approach for variable selection with respect to panel count data. For the problem, we adopt the idea behind the non-concave penalized likelihood approach and develop a non-concave penalized estimating function approach. The proposed methodology selects variables and estimates regression coefficients simultaneously, and an algorithm is presented for this process. We show that the proposed procedure performs as well as the oracle procedure in that it yields the estimates as if the correct submodel were known. Simulation studies are conducted for assessing the performance of the proposed approach. An illustrative example from a cancer study is provided.