Showing papers on "Mixed model published in 1979"
Journal Article•
TL;DR: For pedigree data, the maximum likelihood estimates of the parameters in polygenic and mixed models are derived analytically although not in closed form but in terms of "counting equations" allowing an iterative solution.
Abstract: For pedigree data, the maximum likelihood estimates of the parameters in polygenic and mixed models are derived analytically although not in closed form but in terms of "counting equations" allowing an iterative solution. Likelihood computations, tests of significance, and tests of goodness of fit are presented. Accelerating the (linear) rate of convergence by a very simple method is demonstrated.
89 citations
TL;DR: In this article, the authors used Maximum Likelihood to estimate variances and covariances when selection within fixed subclasses exists and showed the usefulness of maximum likelihood for estimating variances.
67 citations
TL;DR: In this article, the authors suggest alternative methods for finding linear unbiased estimators and present methods for computing sampling variances which are linear functions of the unknown parameter variances, and discuss nonestimability problems resulting from association of such covariates with fixed factors.
Abstract: Blue of estimable linear functions and exact tests of hypotheses concerning such functions usually do not exist in the covariance model with random factors having unknown variances. This is true even in the equal subclass numbers case. This paper suggests alternative methods for finding linear unbiased estimators and presents methods for computing sampling variances which are linear functions of the unknown parameter variances. Also, higher level covariates are defined and nonestimability problems resulting from association of such covariates with fixed factors are discussed.
30 citations
TL;DR: In this article, unbiased tests for linear hypotheses about fixed effects in general balanced normal mixed classification models are considered and in complete families similar ANOVA tests are shown to be uniformly most powerful invariant unbiased.
Abstract: Tests for linear hypotheses about fixed effects in general balanced normal mixed classification models are considered. In complete families similar ANOVA tests are shown to be uniformly most powerful invariant unbiased. In the general case unbiased tests of BABTLETT-Scheffe type are developed and some properties are discussed.
22 citations
TL;DR: In this article, the asymptotic optimality of the restricted maximum likelihood estimates of variance components in the mixed model of analysis of variance was studied and it was shown that such estimates are not only normal but also equivalent to the maximum likelihood in a reasonable sense.
Abstract: In this paper we study the asymptotic optimality of the restricted maximum likelihood estimates of variance components in the mixed model of analysis of variance. Using conceptual design sequences of Miller (1977), under slightly stronger conditions, we show that the restricted maximum likelihood estimates are not only asymptotically normal, but also asymptotically equivalent to the maximum likelihood estimates in a reasonable sense.
17 citations
01 Jan 1979
5 citations
TL;DR: In this article, the mixed effects MANOVA table provided by BMD12V is used to compute Raw Generalized Variances and hence U and F statistics for mixed effects models.
Abstract: Details are given on how to use the mixed effects MANOVA table provided by BMD12V to compute Raw Generalized Variances and hence U and F statistics for mixed effects models.
2 citations