JSM 2004 - Toronto

Abstract #300838

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Activity Number: 436
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #300838
Title: Bayesian Monotone Smoothing
Author(s): Farideh H. Dehkordi-Vakil*+
Companies: Western Illinois University
Address: Stipes Hall 431M, Macomb, IL, 61455,
Keywords: monotone smoothing ; Bayesian ; regression ; WinBugs
Abstract:

The data from many disciplines call for regression on smooth monotone functions. As is well known, the classical pool-adjacent-violators technique of monotonizing does not yield a curve that is necessary smooth, and the classical spline-fitting produces a smooth function, but it is not guaranteed to be monotone. This situation has much improved, during the recent years, thanks to the contributions of such authors as J.O. Ramsay and Xuming He and Peide Shi. The methods these authors use is based on sophisticated optimization techniques and requires codes that are specifically designed for the purpose of this problem. We present a Bayesian approach to this problem, which offers distinct advantage over the optimization methods on at least two grounds. Bayesian framework offers the possibility of a direct estimation of the regression function through sampling methods without the use of optimal criterion. This, in turn, makes our method computationally simpler, for it allows us to use the widely available software, WinBugs, developed for the implementation of Markov chain Monte Carlo method. No special code is needed.

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