Multiple‐change‐point detection for high dimensional time series via sparsified binary segmentation

Time series segmentation, which is also known as multiple‐change‐point detection, is a well‐established problem. However, few solutions have been designed specifically for high dimensional situations. Our interest is in segmenting the second‐order structure of a high dimensional time series. In a ge...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 77; no. 2; pp. 475 - 507
Main Authors:	Cho, Haeran, Fryzlewicz, Piotr
Format:	Journal Article
Language:	English
Published:	Oxford Royal Statistical Society 01.03.2015 Blackwell Publishing Ltd John Wiley & Sons Ltd Oxford University Press
Subjects:	Algorithms Averages Binary segmentation Changes Cumulative sum statistic Dimensionality Economic crises Hierarchical scales High dimensional time series International Locally stationary wavelet model Multiple-change-point detection Multivariate analysis Null hypothesis Segmentation Simulations Standard and Poors 500 Index Standard deviation Statistical analysis Statistics Studies Threshing Thresholding Time Time series time series analysis Time series models wavelet United Kingdom > UK
ISSN:	1369-7412, 1467-9868
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!