|
Activity Number:
|
156
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 7, 2006 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Biometrics Section
|
| Abstract - #305621 |
|
Title:
|
Nonparametric Inference for Panel Count Data
|
|
Author(s):
|
Ying Zhang*+
|
|
Companies:
|
The University of Iowa
|
|
Address:
|
Department of Biostatistics, Iowa City, IA, 52242,
|
|
Keywords:
|
panel count data ; interval censored data ; nonparametric test ; asymptotic distribution
|
|
Abstract:
|
We study a simple nonparametric inference procedure for panel count data, a type of complicated data often appearing in clinical trials. We propose an easy-to-implement nonparametric estimation method for the mean function of counting process by maximizing a pseudo-likelihood function established from a nonhomogeneous Poisson process. We derive the asymptotic normality of a smooth functional of the estimator. This smooth function is estimated easily, hence warranting a useful inference procedure for panel count data. We further propose a simple nonparametric test for the comparison of the mean functions among k independent samples. The test is validated through simulation studies and demonstrated by the two real-life examples.
|
- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
Back to the full JSM 2006 program |
