Partial likelihood process and asymptotic normality
type: Item
instance of: scholarly article
title: Partial likelihood process and asymptotic normality
zbMATH Open document ID: 0632.62088
DOI: 10.1016/0304-4149(87)90050-0
author: Jean Jacod
published in: Stochastic Processes and their Applications
publication date: 1987-01-01T00:00:00Z
review text: The notion of partial likelihood, introduced by D. R. Cox and used by R. Gill, K. Dhzaparidze and others for particular cases, is generalized for binary statistical experiments when observations consist of a stochastic process which is a semimartingale with predescribed characteristics. The idea is to write a formal expression for a likelihood process in spite of the fact that this expression contains as a nuisance parameter a nonobservable process. Nethertheless, it is possible to prove that this partial likelihood process can be used to obtain ``nice'' estimates of the parameter. In the paper a notion of the partial Hellinger process is presented and a result on asymptotic normality of partial likelihood processes is proved.
Mathematics Subject Classification ID: 62M09
Mathematics Subject Classification ID: 60G44
zbMATH DE Number: 4028684
zbMATH Keywords: partial likelihood
zbMATH Keywords: binary statistical experiments
zbMATH Keywords: semimartingale
zbMATH Keywords: likelihood process
zbMATH Keywords: nuisance parameter
zbMATH Keywords: partial likelihood process
zbMATH Keywords: partial Hellinger process
zbMATH Keywords: asymptotic normality
reviewed by: Yuri Kabanov
MaRDI profile type: Publication
full work available at URL: 0304-4149(87)90050-0
OpenAlex ID: W2010981849
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description: scientific article; zbMATH DE number 4028684