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Godambe estimating functions and asymptotic optimal inference

Authors
Hwang, S. Y.Basawa, I. V.
Issue Date
Aug-2011
Publisher
ELSEVIER SCIENCE BV
Keywords
Asymptotic optimality; Godambe estimating functions; Large sample tests; Quasilikelihood estimation
Citation
STATISTICS & PROBABILITY LETTERS, v.81, no.8, pp.1121 - 1127
Journal Title
STATISTICS & PROBABILITY LETTERS
Volume
81
Number
8
Start Page
1121
End Page
1127
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/12527
DOI
10.1016/j.spl.2011.03.006
ISSN
0167-7152
Abstract
Godambe (1985) introduced a class of optimum estimating functions which can be regarded as a generalization of quasilikelihood score functions. The "optimality" established by Godambe (1985) within a certain class is for estimating functions and it is based on finite samples. The question that arises naturally is what (if any) asymptotic optimality properties do the estimators and tests based on optimum estimating functions possess. In this paper, we establish, via presenting a convolution theorem, asymptotic optimality of estimators and tests obtained from Godambe optimum estimating functions. It is noted that we do not require the knowledge of the likelihood function. (C) 2011 Elsevier B.V. All rights reserved.
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