Inference for multivariate normal hierarchical models

• Published in: Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 62; no. 2; pp. 399 - 412
• Main Authors: Everson, P. J., Morris, C. N.
• Format: Journal Article
• Language: English
• Published: Oxford, UK and Boston, USA Blackwell Publishers Ltd 2000; Blackwell Publishers; Blackwell; Royal Statistical Society
• Series: Journal of the Royal Statistical Society Series B
• Subjects: Algorithms; Applications; Bayesian method; Constrained Wishart distributions; Covariance; Covariance matrices; Distribution; Exact sciences and technology; Gaussian distributions; Hierarchy; Importance weighting; Inference; Interval estimates; Interval estimators; Markov processes; Mathematical vectors; Mathematics; Maximum likelihood estimation; Medical profiling; Medical sciences; Multilevel models; Multivariate analysis; Multivariate empirical Bayes procedures; Parametric inference; Probability and statistics; Probability theory and stochastic processes; Rejection sampling; Restricted maximum; Sampling; Sampling distributions; Sciences and techniques of general use; Statistical discrepancies; Statistics; Bayes estimation; Rejection sampling; Wishart distribution; Gaussian distribution; Wishart matrix; Importance weighting; Hierarchical model; Maximum likelihood; Multivariate analysis; Gibbs sampling; Performance
• ISSN: 1369-7412; 1467-9868
• DOI: 10.1111/1467-9868.00239

This paper provides a new method and algorithm for making inferences about the parameters of a two-level multivariate normal hierarchical model. One has observed J p-dimensional vector outcomes, distributed at level 1 as multivariate normal with unknown mean vectors and with known covariance matrices. At level 2, the unknown mean vectors also have normal distributions, with common unknown covariance matrix A and with means depending on known covariates and on unknown regression coefficients. The algorithm samples independently from the marginal posterior distribution of A by using rejection procedures. Functions such as posterior means and covariances of the level 1 mean vectors and of the level 2 regression coefficient are estimated by averaging over posterior values calculated conditionally on each value of A drawn. This estimation accounts for the uncertainty in A, unlike standard restricted maximum likelihood empirical Bayes procedures. It is based on independent draws from the exact posterior distributions, unlike Gibbs sampling. The procedure is demonstrated for profiling hospitals based on patients' responses concerning p = 2 types of problems (non-surgical and surgical). The frequency operating characteristics of the rule corresponding to a particular vague multivariate prior distribution are shown via simulation to achieve their nominal values in that setting.