Abstract #301104

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JSM 2002 Abstract #301104
Activity Number: 243
Type: Topic Contributed
Date/Time: Tuesday, August 13, 2002 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics*
Abstract - #301104
Title: Asymptotic Theory for General Semiparametric M-Estimator
Author(s): Ying Zhang*+
Affiliation(s): University of Central Florida
Address: P.O. Box, 162370, Orlando, Florida, 32816-2370, USA
Keywords: Asymptotic normality ; Convergence rate ; M-estimation ; Semiparametric Model ; Panel count Data
Abstract:

In a general setting of semiparamteric M-estimation procedure, the estimator of nuisance parameter (nonparametric part) may have lower than square-root of n convergence rate, which makes the overall converegnce rate of semiparametric M-estimator lower than the standard square-root of n. In this paper, we present a general theory for deriving the asymptotic normality for M-estimator of the finite-parameter (for example, the regression parameter of the Cox model in survival analysis) , when the M-estimator of the nonparameteric parameter may converge slower than the square-root of n. The theory is applied to semiparametric maximum pseudo-likelihood estimator with panel count data.

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