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

Yan Liu, Hideaki Nagahata, Hirotaka Uchiyama, Masanobu Taniguchi

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)8014-8027
Number of pages14
JournalCommunications in Statistics: Simulation and Computation
Volume46
Issue number10
DOIs
Publication statusPublished - 2017 Nov 26

Keywords

  • Cluster analysis
  • Discriminant analysis
  • Disparity measure
  • High-dimensional data
  • Jackknife-type adjustment
  • Time series data

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation

Fingerprint Dive into the research topics of 'Discriminant and cluster analysis of possibly high-dimensional time series data by a class of disparities'. Together they form a unique fingerprint.

Cite this