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 journalArticle

    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)1-14
    Number of pages14
    JournalCommunications in Statistics: Simulation and Computation
    DOIs
    Publication statusAccepted/In press - 2017 May 18

    Fingerprint

    Cluster analysis
    Discriminant analysis
    Cluster Analysis
    High-dimensional Data
    Time Series Data
    Discriminant Analysis
    Time series
    Classifiers
    Classifier
    Stationary Time Series
    Jackknife
    Financial Data
    Adjustment
    High-dimensional
    Class
    Model

    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

    Cite this

    Discriminant and cluster analysis of possibly high-dimensional time series data by a class of disparities. / Liu, Yan; Nagahata, Hideaki; Uchiyama, Hirotaka; Taniguchi, Masanobu.

    In: Communications in Statistics: Simulation and Computation, 18.05.2017, p. 1-14.

    Research output: Contribution to journalArticle

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