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

^*Corresponding author for this work

School of Fundamental Science and Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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 language	English
Pages (from-to)	8014-8027
Number of pages	14
Journal	Communications in Statistics: Simulation and Computation
Volume	46
Issue number	10
DOIs	https://doi.org/10.1080/03610918.2016.1263732
Publication status	Published - 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

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10.1080/03610918.2016.1263732

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

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.",

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AB - 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.

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KW - Disparity measure

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KW - Jackknife-type adjustment

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

Abstract

Keywords

ASJC Scopus subject areas

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