TREE MODELS FOR DIFFERENCE AND CHANGE DETECTION IN A COMPLEX ENVIRONMENT
成果类型：
Article
署名作者：
Wang, Yong; Ziedins, Ilze; Holmes, Mark; Challands, Neil
署名单位：
University of Auckland
刊物名称：
ANNALS OF APPLIED STATISTICS
ISSN/ISSBN：
1932-6157
DOI：
10.1214/12-AOAS548
发表日期：
2012
页码：
1162-1184
关键词：
摘要：
A new family of tree models is proposed, which we call differential trees. A differential tree model is constructed from multiple data sets and aims to detect distributional differences between them. The new methodology differs from the existing difference and change detection techniques in its nonparametric nature, model construction from multiple data sets, and applicability to high-dimensional data. Through a detailed study of an arson case in New Zealand, where an individual is known to have been laying vegetation fires within a certain time period, we illustrate how these models can help detect changes in the frequencies of event occurrences and uncover unusual clusters of events in a complex environment.
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