Abstract #300000

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JSM 2003 Abstract #300000
Activity Number: 56
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 4:00 PM to 5:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #300000
Title: Bayesian Adaptive Penalized Splines
Author(s): Min Li*+ and Yan Yu
Companies: California State University, Sacramento and University of Cincinnati
Address: MIS Department, Sacramento, CA, 95819-6088,
Keywords: spline ; smoothing ; Bayesian ; reversible jump
Abstract:

We combine features of penalized splines and regression splines and propose a new approach, termed Bayesian adaptive penalized splines to fit curves. We first outline a hierarchical Bayesian approach to penalized splines, using a smoothing parameter to penalize spline coefficients. We then generalize Bayesian penalized splines by adaptively selecting the number and the locations of knots as in regression splines and also using a smoothing parameter to penalize spline coefficients as in penalized splines. The reversible jump Markov chain Monte Carlo technique is used to select the number and the locations of knots. The method is applied to five simulated data sets. Compared with competing methods, the results are highly competitive in terms of MSE.

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