Scan Statistics for Detecting High-Variance Clusters
Scan statistics are mostly used to detect spatial areas or time intervals in which the mean level of a given variable is more important. Sometimes, when this variable is continuous, there is an interest in looking for clusters in which its variability is more important. In this paper, two scan stati...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2016/7591680 |
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| _version_ | 1832563880207843328 |
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| author | Lionel Cucala |
| author_facet | Lionel Cucala |
| author_sort | Lionel Cucala |
| collection | DOAJ |
| description | Scan statistics are mostly used to detect spatial areas or time intervals in which the mean level of a given variable is more important. Sometimes, when this variable is continuous, there is an interest in looking for clusters in which its variability is more important. In this paper, two scan statistics are introduced for identifying clusters of values exhibiting higher variance. Like many classical scan statistics, the first one relies on a generalized likelihood ratio test whereas the second one is based on ratios of empirical variances. These methods are useful to identify spatial areas or time intervals in which the variability of a given variable is more important. In an application of the new methods, I look for geographical clusters of high-variability income in France and then for residuals exhibiting higher variance in a linear regression context. |
| format | Article |
| id | doaj-art-ee084808f9854de98b822fe855f8546e |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-ee084808f9854de98b822fe855f8546e2025-02-03T01:12:26ZengWileyJournal of Probability and Statistics1687-952X1687-95382016-01-01201610.1155/2016/75916807591680Scan Statistics for Detecting High-Variance ClustersLionel Cucala0Institut Montpelliérain Alexander Grothendieck, 34000 Montpellier, FranceScan statistics are mostly used to detect spatial areas or time intervals in which the mean level of a given variable is more important. Sometimes, when this variable is continuous, there is an interest in looking for clusters in which its variability is more important. In this paper, two scan statistics are introduced for identifying clusters of values exhibiting higher variance. Like many classical scan statistics, the first one relies on a generalized likelihood ratio test whereas the second one is based on ratios of empirical variances. These methods are useful to identify spatial areas or time intervals in which the variability of a given variable is more important. In an application of the new methods, I look for geographical clusters of high-variability income in France and then for residuals exhibiting higher variance in a linear regression context.http://dx.doi.org/10.1155/2016/7591680 |
| spellingShingle | Lionel Cucala Scan Statistics for Detecting High-Variance Clusters Journal of Probability and Statistics |
| title | Scan Statistics for Detecting High-Variance Clusters |
| title_full | Scan Statistics for Detecting High-Variance Clusters |
| title_fullStr | Scan Statistics for Detecting High-Variance Clusters |
| title_full_unstemmed | Scan Statistics for Detecting High-Variance Clusters |
| title_short | Scan Statistics for Detecting High-Variance Clusters |
| title_sort | scan statistics for detecting high variance clusters |
| url | http://dx.doi.org/10.1155/2016/7591680 |
| work_keys_str_mv | AT lionelcucala scanstatisticsfordetectinghighvarianceclusters |