Discriminant and cluster analysis of possibly high-dimensional time series data by a class of disparities

Yan Liu; Hideaki Nagahata; Hirotaka Uchiyama; Masanobu Taniguchi

doi:10.1080/03610918.2016.1263732

Discriminant and cluster analysis of possibly high-dimensional time series data by a class of disparities

Yan Liu, Hideaki Nagahata^*, Hirotaka Uchiyama, Masanobu Taniguchi

^*この研究の対応する著者

基幹理工学部

研究成果: Article › 査読

1 被引用数 (Scopus)

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Discriminant and cluster analysis of high-dimensional time series data have been an urgent need in more and more academic fields. To settle the always-existing problem of bias in distance-based classifiers for high-dimensional models, we consider a new classifier with jackknife-type bias adjustment for stationary time series data. The consistency of the classifier is theoretically shown under suitable conditions, including the situations of possibly high-dimensional data. We also conduct the cluster analysis for real financial data.

本文言語	English
ページ（範囲）	8014-8027
ページ数	14
ジャーナル	Communications in Statistics: Simulation and Computation
巻	46
号	10
DOI	https://doi.org/10.1080/03610918.2016.1263732
出版ステータス	Published - 2017 11月 26

ASJC Scopus subject areas

統計学および確率
モデリングとシミュレーション

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10.1080/03610918.2016.1263732

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KW - Discriminant analysis

KW - Disparity measure

KW - High-dimensional data

KW - Jackknife-type adjustment

KW - Time series data

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Discriminant and cluster analysis of possibly high-dimensional time series data by a class of disparities

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