%0 Journal Article
%T Sequential Shrinkage Estimate for COX Regression Models with Uncertain Number of Effective Variables
%A Haibo Lu
%A Juling Zhou
%A Cuiling Dong
%J Modeling and Numerical Simulation of Material Science
%P 47-53
%@ 2164-5353
%D 2021
%I Scientific Research Publishing
%R 10.4236/mnsms.2021.113004
%X In the applications of COX regression models, we always encounter data
sets t<span>hat contain too many variables that
only a few of them contribute to the</span> model. Therefore, it will waste
much more samples to estimate the ˇ°noneffectiveˇ± variables in the inference. In
this paper, we use a sequential procedure for constructing<span><span><span style=\"font-family:;\" \"=\"\"> </span></span></span><span><span><span style=\"font-family:;\" \"=\"\">the
fixed size confidence set for the ˇ°effectiveˇ± parameters to the model based on
an adaptive shrinkage estimate such that the ˇ°effectiveˇ± coefficients can be
efficiently identified with the minimum sample size. Fixed design is considered
for numerical simulation. The strong consistency, asymptotic distributions and
convergence rates of estimates under the fixed design are obtained. In addition,
the sequential procedure is shown to be asymptotically optimal in the sense of
Chow and Robbins (1965).</span></span></span>
%K Sequential Estimate
%K COX Regression Model
%K Stopping Time
%K Minimum Sample Size
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=110718