STATISTICAL INFERENCE OF COVARIANCE CHANGE POINTS IN GAUSSIAN MODEL

JIE CHEN; A. K. GUPTA

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STATISTICAL INFERENCE OF COVARIANCE CHANGE POINTS IN GAUSSIAN MODEL

机译：高斯模型中协方差变化点的统计推断

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摘要

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.

机译：在本文中，我们使用Schwarz信息准则（SIC）研究了m维（m> 1）高斯随机矢量序列的多个协方差变化点的测试和估计。还可以获得无偏的SIC。得出检验统计量的渐近零分布。将结果应用于模拟的二元法向向量序列（m = 2），并成功检测到变化。

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来源
《Statistics》 |2004年第1期|p.17-28|共12页
作者
JIE CHEN; A. K. GUPTA;
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作者单位

Department of Mathematics and Statistics, University of Missouri -Kansas City, Kansas City, MO 64110, USA;

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收录信息美国《科学引文索引》(SCI);
原文格式 PDF
正文语种 eng
中图分类应用数学;
关键词
change-points; information criterion; SIC; asymptotic distribution;

机译：变更点信息准则SIC渐近分布;

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STATISTICAL INFERENCE OF COVARIANCE CHANGE POINTS IN GAUSSIAN MODEL

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