|
Activity Number:
|
276
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
IMS
|
| Abstract - #308953 |
|
Title:
|
Profile-Kernel Likelihood Inference with Diverging Number of Parameters
|
|
Author(s):
|
Clifford Lam*+ and Jianqing Fan
|
|
Companies:
|
Princeton University and Princeton University
|
|
Address:
|
Department of Operations Res and Fin Eng, Princeton, NJ, 08544,
|
|
Keywords:
|
Generalized linear models ; Varying coefficients ; High dimensionality ; Asymptotic normality ; Profile likelihood ; Generalized likelihood ratio tests
|
|
Abstract:
|
We investigate both theoretical and practical sides of profile likelihood estimation and inference for the generalized varying coefficient partially linear model with growing number of predictors. When the number of parameters grows with sample size, the existence and asymptotic normality of the profile likelihood estimator are established under some regularity conditions. Profile likelihood ratio inference for the growing number of parameters is proposed and Wilk's phenomenon is demonstrated. A new algorithm for computing profile-kernel estimator is proposed and investigated. Simulation studies show that the resulting estimates are as efficient as the fully iterative profile-kernel estimates. For moderate sample sizes, our proposed procedure saves much computational time over the fully iterative profile-kernel one and gives more stable estimates. A set of real data is also analyzed.
|
