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 |
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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 | |
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