VALIDITY OF EDGEWORTH EXPANSIONS FOR STATISTICS OF TIME SERIES

Masanobu Taniguchi

doi:10.1111/j.1467-9892.1984.tb00377.x

VALIDITY OF EDGEWORTH EXPANSIONS FOR STATISTICS OF TIME SERIES

Masanobu Taniguchi^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 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 language	English
Pages (from-to)	37-51
Number of pages	15
Journal	Journal of Time Series Analysis
Volume	5
Issue number	1
DOIs	https://doi.org/10.1111/j.1467-9892.1984.tb00377.x
Publication status	Published - 1984
Externally published	Yes

Keywords

circular autoregressive moving average process
Edgeworth expansion
maximum likelihood estimator

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1111/j.1467-9892.1984.tb00377.x

Cite this

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title = "VALIDITY OF EDGEWORTH EXPANSIONS FOR STATISTICS OF TIME SERIES",

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.",

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AB - 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.

KW - circular autoregressive moving average process

KW - Edgeworth expansion

KW - maximum likelihood estimator

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VALIDITY OF EDGEWORTH EXPANSIONS FOR STATISTICS OF TIME SERIES

Abstract

Keywords

ASJC Scopus subject areas

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