VALIDITY OF EDGEWORTH EXPANSIONS FOR STATISTICS OF TIME SERIES

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Abstract. In this paper, we discuss the validity of the multivariate Edgeworth expansion of distribution functions of statistics which need not be standardized sums of independent and identically distributed vectors. We apply this result to statistics of time series. In particular, we shall give the asymptotic expansion of the distribution of the maximum likelihood estimator of a parameter of a circular autoregresive moving average process.

Original languageEnglish
Pages (from-to)37-51
Number of pages15
JournalJournal of Time Series Analysis
Volume5
Issue number1
DOIs
Publication statusPublished - 1984
Externally publishedYes

Fingerprint

Edgeworth Expansion
Time series
Statistics
Moving Average Process
Identically distributed
Maximum Likelihood Estimator
Maximum likelihood
Distribution functions
Asymptotic Expansion
Distribution Function
Edgeworth expansion
Moving average
Maximum likelihood estimator
Asymptotic expansion
Distribution function

Keywords

  • circular autoregressive moving average process
  • Edgeworth expansion
  • maximum likelihood estimator

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

VALIDITY OF EDGEWORTH EXPANSIONS FOR STATISTICS OF TIME SERIES. / Taniguchi, Masanobu.

In: Journal of Time Series Analysis, Vol. 5, No. 1, 1984, p. 37-51.

Research output: Contribution to journalArticle

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