Premium
Variable Selection for Panel Count Data via Non‐Concave Penalized Estimating Function
Author(s) -
TONG XINGWEI,
HE XIN,
SUN LIUQUAN,
SUN JIANGUO
Publication year - 2009
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2009.00658.x
Subject(s) - oracle , count data , mathematics , context (archaeology) , regression analysis , variable (mathematics) , selection (genetic algorithm) , model selection , econometrics , statistics , panel data , feature selection , function (biology) , regression , computer science , artificial intelligence , paleontology , mathematical analysis , software engineering , evolutionary biology , poisson distribution , biology
. Variable selection is an important issue in all regression analyses, and in this paper we discuss this in the context of regression analysis of panel count data. 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 and suggest that it works well for practical situations. An illustrative example from a cancer study is provided.