Sparsity Inducing Prior Distributions for Correlation Matrices of Longitudinal Data.

ABSTRACT: For longitudinal data, the modeling of a correlation matrix R can be a difficult statistical task due to both the positive definite and the 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 pair of prior distributions on the set of correlation matrices for longitudinal data through the partial autocorrelations (PACs), each of which vary independently over [-1,1]. The first prior shrinks each of the PACs toward zero with increasingly aggressive shrinkage in lag. The second prior (a selection prior) is a mixture of a zero point mass and a continuous component for each PAC, allowing for a sparse representation. The structure implied under our priors is readily in

SUBMITTER: Gaskins JT 

PROVIDER: S-EPMC4217169 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature