Nonparametric approach for non-gaussian vector stationary processes

Masanobu Taniguchi*, Madan L. Puri, Masao Kondo

*この研究の対応する著者

研究成果: Article査読

27 被引用数 (Scopus)

抄録

Suppose that {z(t)} is a non-Gaussian vector stationary process with spectral density matrix f(λ). In this paper we consider the testing problem H:∫π K{f(λ)} dλ = c against A: ∫π K{f(λ)} dλ ≠ c, where K{·} is an appropriate function and c is a given constant. For this problem we propose a test Tn based on ∫π K{f̂n(λ)} dλ, where f̂n(λ) is a nonparametric spectral estimator of f(λ), and we define an efficacy of Tn under a sequence of nonparametric contiguous alternatives. The efficacy usually depnds on the fourth-order cumulant spectra fZ4 of z(t). If it does not depend on fZ4, we say that Tn is non-Gaussian robust. We will give sufficient conditions for Tn to be non-Gaussian robust. Since our test setting is very wide we can apply the result to many problems in time series. We discuss interrelation analysis of the components of {z(t)} and eigenvalue analysis of f(λ). The essential point of our approach is that we do not assume the parametric form of f(λ). Also some numerical studies are given and they confirm the theoretical results.

本文言語English
ページ(範囲)259-283
ページ数25
ジャーナルJournal of Multivariate Analysis
56
2
DOI
出版ステータスPublished - 1996 2月
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • 数値解析
  • 統計学、確率および不確実性

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