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Scan statistics for detecting a local change in variance for two-dimensional normal data

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  • Bo Zhao
  • Joseph Glaz

Abstract

In this article scan statistics for detecting a local change in variance for two-dimensional normal data are discussed. When the precise size of the rectangular window, where a local change in variance has occurred, is unknown, multiple and variable window scan statistics are proposed. A simulation study is presented to evaluate the performance of the scan statistics investigated in this article via comparison of power. A method for estimating the rectangular region, where a change in variance has occurred, and the size of the change in variance is also discussed.

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  • Bo Zhao & Joseph Glaz, 2017. "Scan statistics for detecting a local change in variance for two-dimensional normal data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(11), pages 5517-5530, June.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:11:p:5517-5530
    DOI: 10.1080/03610926.2015.1104354
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    Cited by:

    1. Sabine Mercier & Grégory Nuel, 2022. "Duality Between the Local Score of One Sequence and Constrained Hidden Markov Model," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 1411-1438, September.

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