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Statistical inference of covariance change points in gaussian model
Authors:
Jie Chen a;
A. K. Gupta b
| Affiliations: | a Department of Mathematics and Statistics, University of Missouri - Kansas City, Kansas City, MO, USA |
| b Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH, USA |
DOI:
10.1080/0233188032000158817
Publication Frequency:
6 issues per year
Subjects:
Mathematical Statistics;
Statistical Theory & Methods;
Statistics;
Statistics for the Biological Sciences;
Stochastic Models & Processes;
Number of References: 18
Formats available:
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(English)
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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.
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| Keywords: Change-points; Information criterion; SIC; Asymptotic distribution |
| view references (18) |
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