Higher-order asymptotic normality of approximations to the modified signed likelihood ratio statistic for regular models

Heping He*, Thomas A. Severini

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Approximations to the modified signed likelihood ratio statistic are asymptotically standard normal with error of order n-1, where n is the sample size. Proofs of this fact generally require that the sufficient statistic of the model be written as (θ, a), where θ is the maximum likelihood estimator of the parameter θ of the model and a is an ancillary statistic. This condition is very difficult or impossible to verify for many models. However, calculation of the statistics themselves does not require this condition. The goal of this paper is to provide conditions under which these statistics are asymptotically normally distributed to order n-1 without making any assumption about the sufficient statistic of the model.

Original languageEnglish (US)
Pages (from-to)2054-2074
Number of pages21
JournalAnnals of Statistics
Volume35
Issue number5
DOIs
StatePublished - Oct 1 2007

Keywords

  • Cramér-Edgeworth polynomial
  • Edgeworth expansion theory
  • Higher-order normality
  • Modified signed likelihood ratio statistic
  • Sufficient statistic

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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