English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Change-point detection has been a classical problem in statistics andeconometrics. This work focuses on the problem of detecting abruptdistributional changes in the data-generating distribution of a sequence ofhigh-dimensional observations, beyond the first two moments. This has remaineda substantially less explored problem in the existing literature, especially inthe high-dimensional context, compared to detecting changes in the mean or thecovariance structure. We develop a nonparametric methodology to (i) detect anunknown number of change-points in an independent sequence of high-dimensionalobservations and (ii) test for the significance of the estimated change-pointlocations. Our approach essentially rests upon nonparametric tests for thehomogeneity of two high-dimensional distributions. We construct a singlechange-point location estimator via defining a cumulative sum process in anembedded Hilbert space. As the key theoretical innovation, we rigorously deriveits limiting distribution under the high dimension medium sample size (HDMSS)framework. Subsequently we combine our statistic with the idea of wild binarysegmentation to recursively estimate and test for multiple change-pointlocations. The superior performance of our methodology compared to otherexisting procedures is illustrated via extensive simulation studies as well asover stock prices data observed during the period of the Great Recession in theUnited States.

High-dimensional Change-point Detection Using Generalized Homogeneity Metrics