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

Yan Liu, Hideaki Nagahata, Hirotaka Uchiyama, Masanobu Taniguchi

研究成果: Article査読

抄録

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
46
10
DOI
出版ステータスPublished - 2017 11 26

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation

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