Skip Navigation

Biometrika 1995 82(3):479-488; doi:10.1093/biomet/82.3.479
© 1995 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by GELFAND, A. E.
Right arrow Articles by CARLIN, B. P.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Articles

Efficient parametrisations for normal linear mixed models

ALAN E. GELFAND1, SUJIT K. SAHU2 and BRADLEY P. CARLIN3

1 Department of Statistics, University of Connecticut Box U-120, Storrs, Connecticut 06269-3120, U.S.A.
2 Statistical Laboratory, University of Cambridge 16 Mill Lane, Cambridge CB2 1SB, U.K.
3 Division of Biostatistics, School of Public Health, University of Minnesota Box 303 Mayo Memorial Building, Minneapolis, Minnesota 55455-0392, U.S.A.

Received for publication 1 December 1993. Revision received 1 November 1994.
  Abstract

The generality and easy programmability of modern sampling-based methods for maximisation of likelihoods and summarisation of posterior distributions have led to a tremendous increase in the complexity and dimensionality of the statistical models used in practice. However, these methods can often be extremely slow to converge, due to high correlations between, or weak identifiability of, certain model parameters. We present simple hierarchical centring reparametrisations that often give improved convergence for a broad class of normal linear mixed models. In particular, we study the two-stage hierarchical normal linear model, the Laird-Ware model for longitudinal data, and a general structure for hierarchically nested linear models. Using analytical arguments, simulation studies, and an example involving clinical markers of acquired immune deficiency syndrome (aids), we indicate when reparametrisation is likely to provide substantial gains in efficiency.

Key Words: Gibbs sampler • Hierarchical model • Identifiability • Laird-Ware model • Markov chain Monte Carlo • Nested models • Random effects model • Rate of convergence


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 GeneticsHome page
L. Varona, D. Sorensen, and R. Thompson
Analysis of Litter Size and Average Litter Weight in Pigs Using a Recursive Model
Genetics, November 1, 2007; 177(3): 1791 - 1799.
[Abstract] [Full Text] [PDF]

Home page
 Statistical ModellingHome page
S. Toti, A. Biggeri, and A. Baldasseroni
A hierarchical Bayesian model for a variability analysis of measurements of occupational n-hexane exposure in Italy
Statistical Modeling, July 1, 2006; 6(2): 175 - 185.
[Abstract] [PDF]

Home page
 Marketing ScienceHome page
B. J. Bronnenberg and C. F. Mela
Market Roll-Out and Retailer Adoption for New Brands
Marketing Science, January 1, 2004; 23(4): 500 - 518.
[Abstract] [PDF]

Home page
 Stat Methods Med ResHome page
J. S Sithole and P. W Jones
Analysis of a Bayesian repeated measures model for detecting differences in GP prescribing habits
Statistical Methods in Medical Research, December 1, 2003; 12(6): 475 - 487.
[Abstract] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.