Two sample tests for high-dimensional autocovariances

Changryong Baek, Katheleen M. Gates, Benjamin Leinwand, Vladas Pipiras

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The problem of testing for the equality of autocovariances of two independent high-dimensional time series is studied. Tests based on the suprema or sums of suitable averages across the dimensions are adapted from the available literature. Another test based on principal component analysis (PCA) is introduced and studied in theory. An extension is also considered to the setting of testing for the equality of autocovariances of two populations, having multiple individual high-dimensional series from the two populations. The proposed methodologies are assessed on simulated data, with the performance of the introduced PCA testing being superior overall. An application using fMRI data from individuals experiencing two different emotional states is provided.

Original languageEnglish
Article number107067
JournalComputational Statistics and Data Analysis
Volume153
DOIs
StatePublished - Jan 2021

Keywords

  • Autocovariances
  • Block multiplier bootstrap
  • Dynamic factor models
  • High-dimensional time series
  • Hypothesis tests
  • Principal components

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