JSM 2005 - Toronto

Abstract #303014

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 221
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #303014
Title: A Bayesian Approach to Shape-Restricted Inference
Author(s): Mary Meyer*+
Companies: University of Georgia
Address: Statistics Department, Athens, GA, 30605, United States
Keywords: bayesian ; convex regression ; monotone regression ; nonparametric function estimation
Abstract:

Estimating a function nonparametrically using shape restrictions such as monotone or convex is useful in many applications, such as regression analyses, generalized additive models, survival analysis, etc. The maximum likelihood estimator is typically formulated as a projection onto a convex cone, or iteratively reweighted projections. Inference using the maximum likelihood estimator is difficult. A Bayesian formulation provides smooth, shape-restricted function estimates, and inference methods involve sampling from the posterior. Solutions to inference problems that are intractable with maximum likelihood methods are feasible using Bayes credible intervals and Bayes factors.

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Revised March 2005