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Statistical and Computational Aspects of Mixed Model Analysis

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  • Arthur P. Dempster
  • Chandu M. Patel
  • Murray R. Selwyn
  • Arthur J. Roth

Abstract

Statistical and computational techniques for the analysis of data from a normal mixed model with two variances are discussed and illustrated. Two iterative algorithms for restricted maximum likelihood estimation (REML) of the variances are compared. It is shown that these algorithms are much simplified by the use of a preliminary eigenvalue–eigenvector analysis. Two numerical examples are used to illustrate the theory by showing how variance estimates are used in the estimation and testing of fixed effects in the model. Monte Carlo simulations indicate that actual alpha levels of the tests are close to the nominal levels despite the estimation of the variance components. Diagnostic techniques are employed to assess model assumptions.

Suggested Citation

  • Arthur P. Dempster & Chandu M. Patel & Murray R. Selwyn & Arthur J. Roth, 1984. "Statistical and Computational Aspects of Mixed Model Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(2), pages 203-214, June.
    • Handle: RePEc:bla:jorssc:v:33:y:1984:i:2:p:203-214
    DOI: 10.2307/2347446
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    Cited by:

    1. Ofversten, Jukka, 1995. "Estimation in mixed models via layer triangular transformation," Computational Statistics & Data Analysis, Elsevier, vol. 20(6), pages 657-667, December.
    2. Atkinson, A. C. & Koopman, S. J. & Shephard, N., 1997. "Detecting shocks: Outliers and breaks in time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 387-422, October.

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