Statistical inference of covariance change points in gaussian model

Jie Chen, A. K. Gupta

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

In this paper, we study the testing and estimation of multiple covariance change points for a sequence of m-dimensional (m > 1) Gaussian random vectors by using the Schwarz information criterion (SIC). The unbiased SIC is also obtained. The asymptotic null distribution of the test statistic is derived. The result is applied to a simulated bivariate normal vector sequence (m = 2), and changes are successfully detected.

Original languageEnglish (US)
Pages (from-to)17-28
Number of pages12
JournalStatistics
Volume38
Issue number1
DOIs
StatePublished - Feb 1 2004
Externally publishedYes

Fingerprint

Information Criterion
Change Point
Gaussian Model
Statistical Inference
Bivariate Normal
M-sequence
Normal vector
Null Distribution
Random Vector
Asymptotic distribution
Test Statistic
Testing
Information criterion
Statistical inference
Change point
Test statistic

Keywords

  • Asymptotic distribution
  • Change-points
  • Information criterion
  • SIC

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

Cite this

Statistical inference of covariance change points in gaussian model. / Chen, Jie; Gupta, A. K.

In: Statistics, Vol. 38, No. 1, 01.02.2004, p. 17-28.

Research output: Contribution to journalArticle

Chen, Jie ; Gupta, A. K. / Statistical inference of covariance change points in gaussian model. In: Statistics. 2004 ; Vol. 38, No. 1. pp. 17-28.
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