Non-Hierarchical Multilevel Models

  • Jon Rasbash
  • William J. Browne


Primary School MCMC Method Observation Level Data Augmentation Variance Component Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. W. J. Browne and D. Draper. A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Analysis, 1:473–549, 2006. (with discussion).MathSciNetCrossRefGoogle Scholar
  2. W. J. Browne, H. Goldstein, and J. Rasbash. Multiple membership multiple classification (MMMC) models. Statistical Modelling, 1:103–124, 2001.CrossRefGoogle Scholar
  3. J. M. Bull, G. D. Riley, J. Rasbash, and H. Goldstein. Parallel implementation of a multilevel modelling package. Computational Statistics & Data Analysis, 31:457–474, 1999.MATHCrossRefGoogle Scholar
  4. D. G. Clayton and J. Rasbash. Estimation in large crossed random-effect models by data augmentation. Journal of the Royal Statistical Society, Series A, 162:425–436, 1999.Google Scholar
  5. R. Ecochard and D. G. Clayton. Multilevel modelling of conception in artificial insemination by donor. Statistics in Medicine, 17:1137–1156, 1998.CrossRefGoogle Scholar
  6. A. E. Gelfand and A. F. M. Smith. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85:398–409, 1990.MATHCrossRefMathSciNetGoogle Scholar
  7. H. Goldstein. Multilevel mixed linear model analysis using iterative generalized least squares. Biometrika, 73:43–56, 1986.MATHCrossRefMathSciNetGoogle Scholar
  8. H. Goldstein. Multilevel Statistical Models, 3rd edition. Edward Arnold, London, 2003.MATHGoogle Scholar
  9. H. Goldstein and J. Rasbash. Improved approximations for multilevel models with binary responses. Journal of the Royal Statistical Society, Series A, 159:505–513, 1996.MATHMathSciNetGoogle Scholar
  10. H. Goldstein, J. Rasbash, W. J. Browne, G. Woodhouse, and M. Poulain. Multilevel modelling in the study of dynamic household structures. European Journal of Population, 16:373–387, 2000.Google Scholar
  11. A. B. Lawson, W. J. Browne, and C. Vidal-Rodeiro. Disease Mapping using WinBUGS and MLwiN. Wiley, London, 2003.Google Scholar
  12. Y. Lee and J. A. Nelder. Hierarchical generalised linear models: A synthesis of generalised linear models, random-effect models and structured dispersions. Biometrika, 88:987–1006, 2001.MATHCrossRefMathSciNetGoogle Scholar
  13. J. X. Pan and R. Thompson. Generalized linear mixed models: An improved estimating procedure. In J. G. Bethlehem and P. G. M. van der Heijden, editors, COMPSTAT: Proceedings in Computational Statistics, 2000, pages 373–378. Physica, Heidelberg, 2000.Google Scholar
  14. J. Rasbash and W. J. Browne. Modelling non-hierarchical structures. In A. H. Leyland and H. Goldstein, editors, Multilevel Modelling of Health Statistics, pages 93–106. Wiley, New York, 2001.Google Scholar
  15. J. Rasbash and H. Goldstein. Efficient analysis of mixed hierarchical and crossed random structures using a multilevel model. Journal of Behavioural Statistics, 19:337–350, 1994.CrossRefGoogle Scholar
  16. J. Rasbash, F. Steele, W. J. Browne, and B. Prosser. A User's Guide to MLwiN. Version 2.0. Centre for Multilevel Modelling, University of Bristol, Bristol, UK, 2005.Google Scholar
  17. S. W. Raudenbush. A crossed random effects model for unbalanced data with applications in cross-sectional and longitudinal research. Journal of Educational Statistics, 18:321–350, 1993.CrossRefGoogle Scholar
  18. G. Rodríguez and N. Goldman. An assessment of estimation procedures for multi level models with binary responses. Journal of the Royal Statistical Society, Series A, 158:73–89, 1995.Google Scholar
  19. J. L. Schafer. Analysis of Incomplete Multivariate Data. Chapman & Hall, London, 1997.MATHGoogle Scholar
  20. M. A. Tanner and W. H. Wong. The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82:528–550, 1987. (with discussion).MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Jon Rasbash
    • 1
  • William J. Browne
    • 2
  1. 1.Radiation Oncology and BiologyGraduate School of Education University of BristolBristol BS8 1JA
  2. 2.Department of Clinical Veterinary Science Langford HouseUniversity of Bristol LangfordNorth Somerset BS405DU

Personalised recommendations