Local asymptotic normality of a sequential model for marked point processes and its applications

Yoichi Nishiyama*

*この研究の対応する著者

研究成果: Article査読

6 被引用数 (Scopus)

抄録

This paper deals with statistical inference problems for a special type of marked point processes based on the realization in random time intervals [0,τu]. Sufficient conditions to establish the local asymptotic normality (LAN) of the model are presented, and then, certain class of stopping times τu satisfying them is proposed. Using these stopping rules, one can treat the processes within the framework of LAN, which yields asymptotic optimalities of various inference procedures. Applications for compound Poisson processes and continuous time Markov branching processes (CMBP) are discussed. Especially, asymptotically uniformly most powerful tests for criticality of CMBP can be obtained. Such tests do not exist in the case of the non-sequential approach. Also, asymptotic normality of the sequential maximum likelihood estimators (MLE) of the Malthusian parameter of CMBP can be derived, although the non-sequential MLE is not asymptotically normal in the supercritical case.

本文言語English
ページ(範囲)195-209
ページ数15
ジャーナルAnnals of the Institute of Statistical Mathematics
47
2
DOI
出版ステータスPublished - 1995 6 1
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率

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