TITLE:
Automatic Variable Selection for Single-Index Random Effects Models with Longitudinal Data
AUTHORS:
Suigen Yang, Liugen Xue
KEYWORDS:
Variable Selection; Single-Index Model; Random Effects; Longitudinal Data
JOURNAL NAME:
Open Journal of Statistics,
Vol.4 No.3,
April
24,
2014
ABSTRACT:
We consider the problem of variable selection
for the single-index random effects models with longitudinal data. An automatic
variable selection procedure is developed using smooth-threshold. The proposed
method shares some of the desired features of existing variable selection
methods: the resulting estimator enjoys the oracle property; the proposed
procedure avoids the convex optimization problem and is flexible and easy to
implement. Moreover, we use the penalized weighted deviance criterion for a
data-driven choice of the tuning parameters. Simulation studies are carried out
to assess the performance of our method, and a real dataset is analyzed for
further illustration.