A recap on Linear Mixed Models and their hat-matrices

  • Gianfranco Lovison Dipartimento di Scienze Economiche, Aziendali e Statistiche - Università di Palermo
  • Mariangela Sciandra
Keywords: Linear Mixed Models·Inference·Hat matrices·Orthogonal Projectors

Abstract

This working paper has a twofold goal. On one hand, it provides a recap of Linear Mixed Models (LMMs): far from trying to be exhaustive, this first part of the working paper focusses on the derivation of theoretical results on estimation of LMMs that are scattered in the literature or whose mathematical derivation is sometimes missing or too quickly sketched. On the other hand, it discusses various definitions that are available in the literature for the hat-matrix of Linear Mixed Models, showing their limitations and proving their equivalence.

References

Ando, T. (2007). Bayesian predictive information criterion for the evaluation of hierarchical bayesianand empirical bayes models.Biometrika, 443–458.
Breslow, N. E. and D. G. Clayton (1993). Approximate inference in generalized linear mixed models.Journal of the American statistical Association 88(421), 9–25.
Demidenko, E. and T. A. Stukel (2005). Influence analysis for linear mixed-effects models.Statisticsin medicine 24(6), 893–909.
Harville, D. A. (1977). Maximum likelihood approaches to variance component estimation and torelated problems.Journal of the American Statistical Association 72(358), 320–338.
Henderson, C. R., O. Kempthorne, S. R. Searle, and C. Von Krosigk (1959). The estimation ofenvironmental and genetic trends from records subject to culling.Biometrics 15(2), 192–218. A recap on Linear Mixed Models and their hat-matrices 21
Henderson, H. V. and S. R. Searle (1981). On deriving the inverse of a sum of matrices.SiamReview 23(1), 53–60.
Hodges, J. S. (1998). Some algebra and geometry for hierarchical models, applied to diagnostics.Journal of the Royal Statistical Society: Series B (Statistical Methodology) 60(3), 497–536.
Hodges, J. S. and D. J. Sargent (2001). Counting degrees of freedom in hierarchical and other richly-parameterised models.Biometrika, 367–379.
Lindstrom, M. J. and D. M. Bates (1988). Newtonraphson and em algorithms for linear mixed-effectsmodels for repeated-measures data.Journal of the American Statistical Association 83(404), 1014–1022.
Nobre, J. S. and J. M. Singer (2011). Leverage analysis for linear mixed models.Journal of AppliedStatistics 38(5), 1063–1072.
Patterson, H. D. and R. Thompson (1971). Recovery of inter-block information when block sizes areunequal.Biometrika, 545–554.
Robinson, G. K. (1991). That blup is a good thing: the estimation of random effects.Statisticalscience, 15–32.
Searle, J. R. (1992).The rediscovery of the mind. MIT press.
Searle, J. R., J. L. Austin, P. Strawson, H. Grice, N. Chomsky, J. J. Katz, N. Goodman, and H. Putnam(1971).The philosophy of language, Volume 39. Oxford University Press London.
Seber, G. A. (2008).A matrix handbook for statisticians, Volume 15.
John Wiley & Sons.Singer, J. M., J. S. Nobre, and H. C. Sef (2004). Regression models for pretest/posttest data in blocks.Statistical Modelling 4(4), 324–338.
Stein, C. M. (1981). Estimation of the mean of a multivariate normal distribution.The annals ofStatistics, 1135–1151.
Vaida, F. and S. Blanchard (2005). Conditional akaike information for mixed-effects models. Biometrika 92(2), 351–370.
Wei, B.-C., Y.-Q. Hu, and W.-K. Fung (1998). Generalized leverage and its applications. ScandinavianJournal of statistics 25(1), 25–37.
Zewotir, T. and J. S. Galpin (2007). A unified approach on residuals, leverages and outliers in thelinear mixed model.Test 16(1), 58–75.
Zhang, F. (2006).The Schur complement and its applications, Volume 4. Springer Science & BusinessMedia.
Published
2017-12-08