Bayesian Inference on Variance and Covariance Components for Traits Influenced by Maternal and Direct Genetic Effects, Using the Gibbs Sampler

1994 ◽  
Vol 44 (4) ◽  
pp. 193-201 ◽  
Author(s):  
Just Jensen ◽  
Cunchan S. Wang ◽  
Daniel A. Sorensen ◽  
Daniel Gianola
Keyword(s):  
Bayesian Inference ◽  
Gibbs Sampler ◽  
Genetic Effects ◽  
Covariance Components ◽  
Variance And Covariance Components

scholarly journals Mixed model approaches for diallel analysis based on a bio-model

1996 ◽  
Vol 68 (3) ◽  
pp. 233-240 ◽  
Author(s):  
Jun Zhu ◽  
Bruce S. Weir
Keyword(s):  
Mixed Model ◽  
Diallel Analysis ◽  
Worked Examples ◽  
Genetic Effects ◽  
Unbiased Estimation ◽  
Paternal Effects ◽  
Covariance Components ◽  
Variance And Covariance Components ◽  
Parameter Values ◽  
Estimation Of Variance

SummaryA MINQUE(l) procedure, which is minimum norm quadratic unbiased estimation (MINQUE) method with 1 for all the prior values, is suggested for estimating variance and covariance components in a bio-model for diallel crosses. Unbiasedness and efficiency of estimation were compared for MINQUE(l), restricted maximum likelihood (REML) and MINQUE(θ) which has parameter values for the prior values. MINQUE(l) is almost as efficient as MINQUE(θ) for unbiased estimation of genetic variance and covariance components. The bio-model is efficient and robust for estimating variance and covariance components for maternal and paternal effects as well as for nuclear effects. A procedure of adjusted unbiased prediction (AUP) is proposed for predicting random genetic effects in the bio-model. The jack-knife procedure is suggested for estimation of sampling variances of estimated variance and covariance components and of predicted genetic effects. Worked examples are given for estimation of variance and covariance components and for prediction of genetic merits.

Modeling the crashes count using finite mixture models

2019 ◽  
Vol 7 (1) ◽  
pp. 13-27
Author(s):  
Safaa K. Kadhem ◽  
Sadeq A. Kadhim
Keyword(s):  
Bayesian Inference ◽  
Mixture Models ◽  
Gibbs Sampler ◽  
Simulation Study ◽  
Finite Mixture Models ◽  
Synthetic Data ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Model Parameters ◽  
Mcmc Method

"This paper aims at the modeling the crashes count in Al Muthanna governance using finite mixture model. We use one of the most common MCMC method which is called the Gibbs sampler to implement the Bayesian inference for estimating the model parameters. We perform a simulation study, based on synthetic data, to check the ability of the sampler to find the best estimates of the model. We use the two well-known criteria, which are the AIC and BIC, to determine the best model fitted to the data. Finally, we apply our sampler to model the crashes count in Al Muthanna governance.

scholarly journals Estimates for Genetic Variance Components in Reciprocal Recurrent Selection in Populations Derived from Maize Single-Cross Hybrids

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Matheus Costa dos Reis ◽  
José Maria Villela Pádua ◽  
Guilherme Barbosa Abreu ◽  
Fernando Lisboa Guedes ◽  
Rodrigo Vieira Balbi ◽  
...  
Keyword(s):  
Plant Height ◽  
Genetic Variance ◽  
Recurrent Selection ◽  
Reciprocal Recurrent Selection ◽  
Additive Variance ◽  
Lattice Design ◽  
Single Cross ◽  
Covariance Components ◽  
Variance And Covariance Components ◽  
Single Cross Hybrids

This study was carried out to obtain the estimates of genetic variance and covariance components related to intra- and interpopulation in the original populations (C0) and in the third cycle (C3) of reciprocal recurrent selection (RRS) which allows breeders to define the best breeding strategy. For that purpose, the half-sib progenies of intrapopulation (P11and P22) and interpopulation (P12and P21) from populations 1 and 2 derived from single-cross hybrids in the 0 and 3 cycles of the reciprocal recurrent selection program were used. The intra- and interpopulation progenies were evaluated in a10×10triple lattice design in two separate locations. The data for unhusked ear weight (ear weight without husk) and plant height were collected. All genetic variance and covariance components were estimated from the expected mean squares. The breakdown of additive variance into intrapopulation and interpopulation additive deviations (στ2) and the covariance between these and their intrapopulation additive effects (CovAτ) found predominance of the dominance effect for unhusked ear weight. Plant height for these components shows that the intrapopulation additive effect explains most of the variation. Estimates for intrapopulation and interpopulation additive genetic variances confirm that populations derived from single-cross hybrids have potential for recurrent selection programs.

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