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On the principle of MINQUE for the estimation of variance and covariance components
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Fast heritability estimation based on MINQUE and batch training
Heritability, the proportion of phenotypic variance explained by genome-wide single nucleotide polymorphisms (SNPs) in unrelated individuals, is an important measure of the genetic contribution to human diseases and plays a critical role in studying the ...
Xiaoran Tong, Xiaoxi Shen, Tong Wang
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Computation of MINQUE Variance Component Estimates
Journal of the American Statistical Association, 1977Abstract A matrix containing all information sufficient for the computation of MINQUE variance component estimates (Rao 1973) in random and mixed models is exhibited. The use of this matrix leads to an efficient computer algorithm which saves both computer time and memory usage.
Lon-Mu Liu
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A Method for Calculating MINQUE Estimators of Variance Components
Journal of the American Statistical Association, 1983Abstract The original expression for MINQUE estimators of variance components involved the inverse of a matrix whose dimensions equalled the total sample size. Later papers described more practical methods of computing the estimators. In this article a method is described that involves matrices and vectors with dimensions no larger than the number of ...
J. Kaplan
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MINQUE and ANOVA Estimator for One-way Classification - a Risk Comparison
Biometrical Journal, 1978AbstractFor the one‐way classification in unbalanced case MINQUEstimator for components of variance are given in a more explicit form than it is done in the paper from C. R. RAO (1971). By means of the risk functions we compare MINQUE and ANOVA estimator.
H. Ahrens
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A Regression Interpretation of the Computation of MINQUE Variance Component Estimates
Journal of the American Statistical Association, 1980Abstract Liu and Senturia (1977) proved that the MINQUE of variance components in the general mixed analysis of variance can be computed in O(n) time, with n the number of observations. This can be proved in a simpler way by interpreting the intermediate steps in the computation as regression residuals.
T. Wansbeek
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