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The Information Systems and Statistics Research Seminar Series
Presented by the Paul H. Chook Department of Information Systems and Statistics
Change-Point Estimation using Shape-Restricted Regression Splines
Dr. Xiyue Liao, NYU
Tuesday, April 4, 2017, 12:30-1:45pm
NVC 11-217, IS-STA Conference Room
From: Prof. Rongning Wu
Paul H. Chook Department of Information Systems and Statistics, Coordinator, Ph.D. program in Information Systems
Change-Point estimation is in need in fields like climate change, signal processing, economics, dose-response analysis etc., but it has not yet been fully discussed. We consider estimating a regression function and its change-point m, where m is a mode or an inflection point. Linear inequality constraints are used with spline regression functions to estimate m and the regression function simultaneously using profile methods. For a given m, the maximum-likelihood estimate of the regression function is found using constrained regression methods, then the set of possible change-points is searched to find the m that maximizes the likelihood. Convergence rates are obtained for each type of change-point estimator, and we show an oracle property, that the convergence rate of the regression function estimator is as if m were known.