Asymptotic expansion of the log-likelihood function based on stopping times defined on a Markov process

Michael G. Akritas, G. G. Roussas

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Consider the parameter space Θ which is an open subset of ℝ k, k≧1, and for each θ∈Θ, let the r.v.′s Y n, n=0, 1, ... be defined on the probability space (X, A, P θ) and take values in a Borel set S of a Euclidean space. It is assumed that the process {Y n }, n≧0, is Markovian satisfying certain suitable regularity conditions. For each n≧1, let υ n be a stopping time defined on this process and have some desirable properties. For 0 < τ n → ∞ as n→∞, set {Mathematical expression} h n →h ∈R k, and consider the log-likelihood function {Mathematical expression} of the probability measure {Mathematical expression} with respect to the probability measure {Mathematical expression}. Here {Mathematical expression} is the restriction of P θ to the σ-field induced by the r.v.′s Y 0, Y 1, ..., {Mathematical expression}. The main purpose of this paper is to obtain an asymptotic expansion of {Mathematical expression} in the probability sense. The asymptotic distribution of {Mathematical expression}, as well as that of another r.v. closely related to it, is obtained under both {Mathematical expression} and {Mathematical expression}.

Original languageEnglish (US)
Pages (from-to)21-38
Number of pages18
JournalAnnals of the Institute of Statistical Mathematics
Volume31
Issue number1
DOIs
StatePublished - Dec 1 1979

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)

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