JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 352
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #305874
Title: A Prior for Partial Autocorrelation Selection
Author(s): Jeremy Gaskins*+ and Michael Daniels
Companies: University of Florida and University of Florida
Address: , Gainesville, FL, 32611,
Keywords: correlation matrix ; longitudinal data ; Bayesian methods ; covariance selection ; partial autocorrelations

Modeling a correlation matrix can be a difficult statistical task due to the positive definite and unit diagonal constraints. Because the number of parameters increases quadratically in the dimension, it is often useful to consider a sparse parameterization. We introduce a prior on the set of correlation matrices through the set of partial autocorrelations (PACs), each of which vary independently over [-1,1]. The prior for each PAC is a mixture of a zero point mass and a continuous piece, allowing for a sparse representation. The structure implied under our prior is interpretable because each zero PAC implies a conditional independence relationship in the data distribution. The PAC priors are compared to standard methods through a simulation study and a multivariate probit data example.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.