A Characterization of Admissible Linear Estimator of Regression Coefficients in Variance Component Models

2011 ◽  
Vol 58-60 ◽  
pp. 1162-1167
Author(s):  
Shi Qing Wang ◽  
Ming Qi Li
Keyword(s):  
Variance Component ◽  
Risk Function ◽  
Sufficient Conditions ◽  
Regression Coefficients ◽  
Linear Estimator ◽  
Linear Combinations ◽  
Linear Estimators ◽  
Necessary And Sufficient ◽  
Variance Component Models ◽  
Component Models

In the paper, for the variance component models we take the ordinary quadratic risk function, and consider the admissibility of the linear estimators of linear combinations of regression coefficients in the class of linear homogeneous and inhomogeneous estimators. We get the necessary and sufficient conditions for the linear estimators of linear combinations of regression coefficients to be admissible.

The Analytic Identification of Variance Component Models Common to Behavior Genetics

2021 ◽  
Author(s):  
Michael D. Hunter ◽  
S. Mason Garrison ◽  
S. Alexandra Burt ◽  
Joseph L. Rodgers
Keyword(s):  
Variance Component ◽  
Behavior Genetics ◽  
Variance Component Models ◽  
Component Models

Bayesian Methods for Variance Component Models

1996 ◽  
Vol 91 (434) ◽  
pp. 743-752 ◽  
Author(s):  
Li Sun ◽  
John S. J. Hsu ◽  
Irwin Guttman ◽  
Tom Leonard
Keyword(s):  
Bayesian Methods ◽  
Variance Component ◽  
Variance Component Models ◽  
Component Models

scholarly journals How to deal with genotype uncertainty in variance component quantitative trait loci analyses

2011 ◽  
Vol 93 (5) ◽  
pp. 333-342 ◽  
Author(s):  
XIA SHEN ◽  
LARS RÖNNEGÅRD ◽  
ÖRJAN CARLBORG
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Variance Component ◽  
Large Animal ◽  
Trait Loci ◽  
Sib Families ◽  
Variance Component Estimates ◽  
Variance Component Models ◽  
Component Models

SummaryDealing with genotype uncertainty is an ongoing issue in genetic analyses of complex traits. Here we consider genotype uncertainty in quantitative trait loci (QTL) analyses for large crosses in variance component models, where the genetic information is included in identity-by-descent (IBD) matrices. An IBD matrix is one realization from a distribution of potential IBD matrices given available marker information. In QTL analyses, its expectation is normally used resulting in potentially reduced accuracy and loss of power. Previously, IBD distributions have been included in models for small human full-sib families. We develop an Expectation–Maximization (EM) algorithm for estimating a full model based on Monte Carlo imputation for applications in large animal pedigrees. Our simulations show that the bias of variance component estimates using traditional expected IBD matrix can be adjusted by accounting for the distribution and that the calculations are computationally feasible for large pedigrees.

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