Abstract #301360

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JSM 2003 Abstract #301360
Activity Number: 85
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #301360
Title: Shrinkage Priors on the Spectral Density for Temporal Data
Author(s): Carsten H. Botts*+ and Michael J. Daniels
Companies: Iowa State University and University of Florida
Address: 701 SW 62nd Blvd., Gainesville, FL, 32607,
Keywords: shrinkage priors ; spectral density ; ARMA ; semiparametric
Abstract:

We propose a shrinkage estimator of the spectral density of temporal processes based on a two-level model. For the first level, we construct an approximate likelihood based on the asymptotic distribution of the periodogram. At the second level, the ordinates of the spectral density at the Fourier frequencies are given priors which 'shrink' them towards a parametric spectral density with the data determining the amount of shrinkage. These estimators are shown to be consistent under misspecification of the parametric spectral density. We compare our estimator to parametric estimators (ARMA models), a nonparametric smoother, and a nonparametric Bayesian estimator.

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