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Activity Number: 402 - Variance, Change Points, and Outliers
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #322519
Title: Asymptotic Distribution Free Change-Point Detection for Modern Data
Author(s): Lynna Chu* and Hao Chen
Companies: University of California, Davis and University of California, Davis
Keywords: change-point ; graph-based tests ; nonparametrics ; scan statistic ; high-dimensional data ; non-Euclidean data

We consider the testing and estimation of change-points, locations where the distribution abruptly changes, in a sequence of multivariate or non-Euclidean observations. We study a nonparametric framework that utilizes similarity information among observations and can thus be applied to any data types as long as an informative similarity measure on the sample space can be defined. The existing approach along this line has low power and/or biased estimates for change-points under some common scenarios. We address these problems by considering new tests based on the similarity information. The new approaches exhibit substantial improvements in detecting and estimating change-points accurately through simulation studies. In addition, the new test statistics are asymptotically distribution free under the null of no change regardless of the choice of similarity. Accurate analytic p-value approximations to the significance of the new test statistics for both single change-point and changed interval alternatives are provided, making them easy off-the-shelf approaches to large datasets. The new approaches are illustrated on a real dataset to detect changes in New York taxi traffic.

Authors who are presenting talks have a * after their name.

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