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A two-stage multiple change-point detection

Title:A two-stage multiple change-point detection

Speaker:Professor Shi Xiaoping,Thompson Rivers University

Time:Dec 22,2016,15:30

Address:K2-526

 

Abstract:

A change point refers to a location or time at which observations or data obey two different models: before and after. These studies of change-point problems have found applications in a wide range of areas, including quality control, finance, environmetrics, medicine, genetics and geography. We propose a procedure for detecting multiple change-points in a mean-shift model. We first convert the change-point problem into a variable selection problem by partitioning the data sequence into several segments. Then, we apply a modified variance inflation factor regression algorithm to each segment in sequential order. When a segment that is suspected of containing a change-point is found, we use a weighted cumulative sum to test if there is indeed a change-point in this segment. Two real data examples including a barcode image and a genetic dataset are illustrated for change-point detection.