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Abstract Details
Activity Number:
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173
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #305318 |
Title:
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Graph-Based Change-Point Detection
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Author(s):
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Hao Chen*+ and David Oliver Siegmund and Nancy R. Zhang
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Companies:
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Stanford University and Stanford University and The Wharton School
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Address:
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Department of Statistics, Stanford, CA, 94305, United States
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Keywords:
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change-point model ;
graph-based test ;
high dimensional data ;
nonparametric
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Abstract:
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Given a sequence of independent observations, we are concerned with testing for no change in distribution versus alternatives with a square-wave change. When the observations are in low dimension and from a known parametric family, existing parametric approaches can be applied. However, for non-Euclidean or high-dimensional observations, the change-point problem has not been well studied. We develop a general framework for change-point detection under all scenarios as long as the metric space on the observations is well defined. We suggest a statistic based on a graph that could reflect the similarity of the data points, such as a minimum spanning tree or a minimum distance pairing graph. An analytic approximation for the false positive error probability is derived and shown to be reasonably accurate by simulation.
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