DISCRIMINANT ANALYSIS FOR STATIONARY VECTOR TIME SERIES

Guoqiang Zhang, Masanobu Taniguchi

研究成果: Article査読

22 被引用数 (Scopus)

抄録

Abstract. In this paper, we shall consider the case where a stationary vector process {Xt} belongs to one of two categories described by two hypotheses π1 and π2. These hypotheses specify that {Xt} has spectral density matrices f(Λ) and g(Λ) under π1 and π2, respectively. Although Gaussianity of {Xt} is not assumed, we can formally make the Gaussian likelihood ratio (GLR) based on X(1),…X(T). Then an approximation I(f:g) of the GLR is given in terms of f(Λ) and g(Λ). If f(Λ) and g(Λ) are known, we can use I(f:g) as a classification statistic. It is shown that I(f:g) is a consistent classification criterion in the sense that the misclassification probabilities converge to zero as T→∝. When g is contiguous to f, we discuss non‐Gaussian robustness of I(f:g). A sufficient condition for the non‐Gaussian robustness will be given. Also a numerical example will be given.

本文言語English
ページ(範囲)117-126
ページ数10
ジャーナルJournal of Time Series Analysis
15
1
DOI
出版ステータスPublished - 1994 1
外部発表はい

ASJC Scopus subject areas

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

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