Local asymptotic normality and mixed normality for Markov statistical models
type: Item
instance of: scholarly article
title: Local asymptotic normality and mixed normality for Markov statistical models
zbMATH Open document ID: 0685.60016
DOI: 10.1007/BF01207516
author: Jean Jacod
author: Lucia Ladelli
author: R. Höpfner
publication date: 1990-01-01T00:00:00Z
review text: We prove local asymptotic normality (resp. local asymptotic mixed normality) of a statistical experiment, when the observation is a positive-recurrent (resp. null-recurrent, with an additional technical assumption) Markov chain or Markov step process, under rather mild regularity assumptions on the transition kernel for Markov chains, and on the infinitesimal generator for Markov processes. The proof makes intensive use of Hellinger processes, thus avoiding almost completely to study the more complicated structure of the likelihoods themselves.
Mathematics Subject Classification ID: 60E05
Mathematics Subject Classification ID: 62E10
Mathematics Subject Classification ID: 60J35
zbMATH DE Number: 4122943
zbMATH Keywords: local asymptotic normality
zbMATH Keywords: statistical experiment
zbMATH Keywords: transition kernel for Markov chains
zbMATH Keywords: infinitesimal generator
zbMATH Keywords: Hellinger processes
MaRDI profile type: Publication
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full work available at URL: https://doi.org/10.1007/bf01207516
OpenAlex ID: W2041996178
published in: Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete
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description: scientific article; zbMATH DE number 4122943