Skip to main content

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • N. E. Breslow and D. G. Clayton. Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88:9–25, 1993.

    Article  MATH  Google Scholar 

  • R. J. Carroll, D. Ruppert, and L. A. Stefanski. Measurement Error in Nonlinear Models. Chapman & Hall, London, 1995.

    MATH  Google Scholar 

  • D. G. Clayton and J. Rasbash. Estimation in large crossed random-effect models by data augmentation. Journal of the Royal Statistical Society, Series A, 162:425–448, 1999. (with discussion).

    Google Scholar 

  • A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39:1–38, 1977. (with discussion).

    MATH  MathSciNet  Google Scholar 

  • A. P. Dempster, D. B. Rubin, and R. K. Tsutakawa. Estimation in covariance components models. Journal of the American Statistical Association, 76:341–353, 1981.

    Article  MATH  MathSciNet  Google Scholar 

  • EM and related algorithms (special issue). Statistica Sinica, 5(1):1–107, 1995.

    Google Scholar 

  • A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis. Chapman & Hall, London, 1995.

    Google Scholar 

  • H. Goldstein. Multilevel Statistical Models, 3rd edition. Edward Arnold, London, 2003.

    MATH  Google Scholar 

  • M. J. R. Healy and M. Westmacott. Missing values in experiments analyzed on automatic computers. Applied Statistics, 5:203–206, 1956.

    Article  Google Scholar 

  • M. J. Lindstrom and D. M. Bates. Newton-Raphson and EM algorithms for linear mixed-effects models for repeated-measures data. Journal of the American Statistical Association, 83:1014–1022, 1988.

    Article  MATH  MathSciNet  Google Scholar 

  • R. J. A. Little and D. B. Rubin. Statistical Analysis with Missing Data. Wiley, New York, 1987.

    MATH  Google Scholar 

  • N. T. Longford. A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects. Biometrika, 74:817–827, 1987.

    Article  MATH  MathSciNet  Google Scholar 

  • N. T. Longford. Random Coefficient Models. Oxford University Press, Oxford, UK, 1993.

    MATH  Google Scholar 

  • N. T. Longford. Logistic regression with random coefficients. Computational Statistics & Data Analysis, 17:1–15, 1994.

    Article  MATH  Google Scholar 

  • N. T. Longford. Reliability of essay rating and score adjustment. Journal of Educational and Behavioral Statistics, 19:171–201, 1994.

    Article  Google Scholar 

  • N. T. Longford. Missing Data and Small-Area Estimation. Modern Analytical Equipment for the Survey Statistician. Springer, New York, 2005.

    Google Scholar 

  • T. A. Louis. Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, Series B, 44:226–233, 1982.

    MATH  MathSciNet  Google Scholar 

  • I. Meilijson. A fast improvement to the EM algorithm on its own terms. Journal of the Royal Statistical Society, Series B, 51:127–138, 1989.

    MATH  MathSciNet  Google Scholar 

  • X.-L. Meng and D. B. Rubin. Using EM to obtain asymptotic variance-covariance matrices: The SEM algorithm. Journal of the American Statistical Association, 86:899–909, 1991.

    Article  Google Scholar 

  • X.-L. Meng and D. B. Rubin. Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika, 80:267–278, 1993.

    Article  MATH  MathSciNet  Google Scholar 

  • X.-L. Meng and D. lowercaseVan Dyk. The EM algorithm — an old folk-song sung to a fast new tune. Journal of the Royal Statistical Society, Series B, 59:511–567, 1997. (with discussion).

    Article  MATH  Google Scholar 

  • T. Orchard and M. A. Woodbury. A missing information principle: Theory and applications. In L. M. Le Cam, J. Neyman, and E. L. Scott, editors, Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, volume 1, pages 697–715. University of California Press, Berkeley, 1972.

    Google Scholar 

  • D. B. Rubin. Inference and missing data. Biometrika, 63:581–592, 1976. (with discussion).

    Article  MATH  MathSciNet  Google Scholar 

  • D. B. Rubin. Noniterative least squares estimates, standard errors and F-tests for analyses of variance with missing data. Journal of the Royal Statistical Society, Series B, 38:270–274, 1976.

    MATH  Google Scholar 

  • D. B. Rubin. Multiple Imputation for Nonresponse in Surveys. Wiley, New York, 1987.

    Google Scholar 

  • D. B. Rubin. Multiple imputation after 18+ years. Journal of the American Statistical Association, 91:473–489, 1996.

    Article  MATH  Google Scholar 

  • J. L. Schafer. Analysis of Incomplete Multivariate Data. Chapman & Hall, London, 1997.

    MATH  Google Scholar 

  • S. lowercaseVan Buuren, H. C. Boshuisen, and D. L. Knook. Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in Medicine, 18:681–694, 1999.

    Article  Google Scholar 

  • C. F. J. Wu. On the convergence properties of the EM algorithm. Annals of Statistics, 11:95–103, 1983.

    Article  MATH  MathSciNet  Google Scholar 

  • F. Yates. The analysis of replicated experiments when the field results are incomplete. Empirical Journal of Experimental Agriculture, 1:129–142, 1933.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Longford, N.T. (2008). Missing Data. In: Leeuw, J.d., Meijer, E. (eds) Handbook of Multilevel Analysis. Springer, New York, NY. https://doi.org/10.1007/978-0-387-73186-5_10

Download citation

Publish with us

Policies and ethics