Discrimination and clustering for multivariate time series

Yoshihide Kakizawa*, Robert H. Shumway, Masanobu Taniguchi

*この研究の対応する著者

研究成果: Article査読

212 被引用数 (Scopus)

抄録

Minimum discrimination information provides a useful generalization of likelihood methodology for classification and clustering of multivariate time series. Discrimination between different classes of multivariate time series that can be characterized by differing covariance or spectral structures is of importance in applications occurring in the analysis of geophysical and medical time series data. For discrimination between such multivariate series, Kullback-Leibler discrimination information and the Chernoff information measure are developed for the multivariate non-Gaussian case. Asymptotic error rates and limiting distributions are given for a generalized spectral disparity measure that includes the foregoing criteria as special cases. Applications to problems of clustering and classifying earthquakes and mining explosions are given.

本文言語English
ページ(範囲)328-340
ページ数13
ジャーナルJournal of the American Statistical Association
93
441
DOI
出版ステータスPublished - 1998 3月 1
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • 統計学、確率および不確実性

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