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Submitted by pscully on Fri, 02/04/2011 - 09:53.
02/10/2011 - 16:10
02/10/2011 - 17:30
STA/BST 290: George Tiao (U Chicago)
Model Heterogeneity in Data Analysis: Detecting Clusters and Outliers via Cross-Validating Predictive Distributions
Thursday, February 10th, 2011 at 4.10pm, MSB 1147 (Colloquium room)
Refreshments: 3.30pm, MSB 4110 (Statistics Lounge)
Speaker: George Tiao (Prof. Emeritus, School of Business, University of Chicago)
Title: Model Heterogeneity in Data Analysis: Detecting Clusters and Outliers via Cross-Validating Predictive Distributions
Abstract: This manuscript presents a procedure for detecting heterogeneity in a sample with respect to a given model. It can be applied to find if a univariate sample or a multivariate sample has been generated by different distributions, or if a regression equation is really a mixture of different regression lines. Based on some special features of cross-validating predictive distributions, the idea of the procedure is first to split the sample into more homogeneous groups and second to recombine the observations in order to form homogeneous clusters. These two phases, splitting and recombining, form the core of the procedure. The proposed procedure is exploratory and can be applied to find heterogeneity in any statistical model. The performance of the procedure is illustrated in univariate, multivariate and linear regression problems.