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2002•SSRN Electronic JournalOpen access

MATLAB algorithm mixed.m for solving Henderson's mixed model equations

Viktor Witkovsk

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Abstract

The MATLAB algorithm mixed.m estimates the parameters of the mixed linear model by using the Henderson’s MMEs (mixed model equations) algorithm, for more details see Searle et al. (1992, pp. 275{286). The algorithm computes the BLUE or the two-stage GLSE, the BLUP or the EBLUP, and the ML, REML, MINQE(I), or MINQE(U,I) of the xed eects, the random eects, and the variance components, respectively, in the mixed linear model, together with the generalized inverse of the MMEs coecient matrix and the Fisher information matrix for the estimated variance components. This allows the user to make the statistical inference (which is at least asymptotically correct) on the set of linear combinations of the

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What this paper is about

The MATLAB algorithm mixed.m estimates the parameters of the mixed linear model by using the Henderson’s MMEs (mixed model equations) algorithm, for more details see Searle et al. (1992, pp. 275{286). The algorithm computes the BLUE or the two-stage GLSE, the BLUP or the EBLUP, and the ML, REML, MINQE(I), or MINQE(U,I) of the xed eects, the random eects, and the variance components, respectively, in the mixed linear model, together with the generalized inverse of the MMEs coecient matrix and the Fisher information matrix for the estimated variance components. This allows the user to make the statistical inference (which is at least asymptotically correct) on the set of linear combinations of the

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Available abstract

The MATLAB algorithm mixed.m estimates the parameters of the mixed linear model by using the Henderson’s MMEs (mixed model equations) algorithm, for more details see Searle et al. (1992, pp. 275{286). The algorithm computes the BLUE or the two-stage GLSE, the BLUP or the EBLUP, and the ML, REML, MINQE(I), or MINQE(U,I) of the xed eects, the random eects, and the variance components, respectively, in the mixed linear model, together with the generalized inverse of the MMEs coecient matrix and the Fisher information matrix for the estimated variance components. This allows the user to make the statistical inference (which is at least asymptotically correct) on the set of linear combinations of the

Key concepts: Mixed model, Generalized linear mixed model, Restricted maximum likelihood, Best linear unbiased prediction, Random effects model, Mathematics, MATLAB, Matrix (chemical analysis)

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