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Activity Number: 280 - Leading the Stream: Novel Methods for Streaming Data
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #325462
Title: Sequential Change-Point Detection Based on Nearest Neighbors
Author(s): Hao Chen*
Companies: University of California, Davis
Keywords: graph-based tests; high-dimensional data; non-Euclidean data; tail probability; average run length; scan statistic

As we observe the dynamics of social networks over time, how can we tell if a significant change happens? We propose a new framework for the detection of change-points as data are generated. The approach utilizes nearest neighbor information and can be applied to ongoing sequences of multivariate data or object data. Different stopping times are compared and one relies on recent observations is recommended. An accurate analytic approximation is obtained for the average run length when there is no change, facilitating its application to real problems.

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

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