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 employed 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 chain Monte Carlo sampling scheme that is computationally efficient and can be applied to covariance matrices of high dimension. It is demonstrated through simulations that the proposed method compares favorably in terms of statistical efficiency with a highly regarded competing approach. The estimator is applied to three real examples in which the dimension of the covariance matrix is large relative to the sample size. The first two are from biometry and electricity demand modeling and are longitudinal. The third is from finance and highlights the potential of our method for estimating cross-sectional covariance matrices.
- Bayesian model,
- Cholesky decomposition,
- High-dimensional covariance matrix,
- Markov chain Monte Carlo,
- Model averaging
Available at: http://works.bepress.com/michael_smith/9/