JSM 2005 Online Program

Abstract #304671

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Activity Number:	51
Type:	Invited
Date/Time:	Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor:	Section on Statistics and the Environment
Abstract - #304671
Title:	Categorizing Continuous Explanatory Variables Using Nonparametric Regression
Author(s):	Sean M. O'Brien*+
Companies:	Duke University
Address:	PO Box 17969, Durham , NC, 27715,
Keywords:	Cutpoint selection ; Changepoint modeling ; Dynamic programming ; Optimal categorization ; Smoothing
Abstract:	This paper extends previous methods for selecting the number and location of cutpoints when a continuous explanatory variable needs to be categorized in a regression setting. One promising new method consists of two steps: first, a nonparametric smoother is used to estimate the unknown regression function and scale parameter; second, these estimates are plugged into an asymptotic formula to find cutpoints that approximately minimize a weighted mean-squared error (WMSE) criterion. Using a computationally efficient dynamic programming algorithm (DPA), the method proposed here chooses cutpoints that exactly minimize the WMSE criterion. Unlike its predecessors, this method can be easily extended to a variety of settings, including the analysis of multivariate and multinomial responses and stratified analyses. The method is illustrated using data from recent environmental, epidemiology, and medical studies. Software is available at www.duke.edu/~obrie027.

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