Journal article

Parsimonious covariance matrix estimation for longitudinal data

M Smith, R Kohn

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | AMER STATISTICAL ASSOC | Published : 2002

Abstract

This article proposes a data-driven method to identify parsimony in the covariance matrix of longitudinal data and to exploit any such parsimony to produce a statistically efficient estimator of the covariance matrix. The approach parameterizes the covariance matrix through the Cholesky decomposition of its inverse. For longitudinal data, this is a one-step-ahead predictive representation, and the Cholesky factor is likely to have off-diagonal elements that are zero or close to zero. A hierarchical Bayesian model is used to identify any such zeros in the Cholesky factor, similar to approaches that have been successful in Bayesian variable selection. The model is estimated using a Markov chai..

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University of Melbourne Researchers