Skip to main content
padlock icon - secure page this page is secure

Inference for multivariate normal hierarchical models

Buy Article:

The full text article is temporarily unavailable.

We apologise for the inconvenience. Please try again later.

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.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Constrained Wishart distributions; Importance weighting; Interval estimates; Medical profiling; Multivariate empirical Bayes procedures; Rejection sampling; Restricted maximum

Document Type: Research Article

Affiliations: 1: Swarthmore College, USA 2: Harvard University, Cambridge, USA

Publication date: February 1, 2000

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more