• Asian-Australasian Journal of Animal Sciences
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• v.31 no.12
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• pp.1863-1870
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• 2018

Objective: The Bayesian first-order antedependence models, which specified single nucleotide polymorphisms (SNP) effects as being spatially correlated in the conventional BayesA/B, had more accurate genomic prediction than their corresponding classical counterparts. Given advantages of $BayesC{\pi}$ over BayesA/B, we have developed hyper-$BayesC{\pi}$, ante-$BayesC{\pi}$, and ante-hyper-$BayesC{\pi}$ to evaluate influences of the antedependence model and hyperparameters for $v_g$ and $s_g^2$ on $BayesC{\pi}$.Methods: Three public data (two simulated data and one mouse data) were used to validate our proposed methods. Genomic prediction performance of proposed methods was compared to traditional $BayesC{\pi}$, ante-BayesA and ante-BayesB. Results: Through both simulation and real data analyses, we found that hyper-$BayesC{\pi}$, ante-$BayesC{\pi}$ and ante-hyper-$BayesC{\pi}$ were comparable with $BayesC{\pi}$, ante-BayesB, and ante-BayesA regarding the prediction accuracy and bias, except the situation in which ante-BayesB performed significantly worse when using a few SNPs and ${\pi}=0.95$. Conclusion: Hyper-$BayesC{\pi}$ is recommended because it avoids pre-estimated total genetic variance of a trait compared with $BayesC{\pi}$ and shortens computing time compared with ante-BayesB. Although the antedependence model in $BayesC{\pi}$ did not show the advantages in our study, larger real data with high density chip may be used to validate it again in the future.

• Journal of Korean Society for Quality Management
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• v.30 no.2
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• pp.118-129
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• 2002

A multiple test of a mean parameter, λ, in the Poisson model is considered using the Bayes factor. Under noninformative improper priors, the intrinsic Bayes factor(IBF) of Berger and Pericchi(1996) and the fractional Bayes factor(FBF) of O'Hagan(1995) called as the default or automatic Bayes factors are used to select one among three models, M$_1$: λ< $λ_0, M$_2$: λ= $λ_0, M$_3$: λ> $λ_0. Posterior probability of each competitive model is computed using the default Bayes factors. Finally, theoretical results are applied to simulated data and real data.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1123-1134
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• 2007

This article addresses the problem of testing whether the shape parameters in k independent Weibull populations are equal. We propose a Bayesian model selection procedure for equality of the shape parameters. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian model selection procedure based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real example are provided.

• Journal of Korean Society for Quality Management
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• v.13 no.1
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• pp.2-11
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• 1985

We study some Bayes esimators of the reliability of k out of m stress-strength model under quadratic loss and various prior distributions. We obtain Bayes estimators, Bayes risk, predictive bounds and asymtotic distribution of Bayes estimator. We investigate behaviours of Bayes estimator in moderate samples.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.1009-1019
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• 2006

In this paper, we consider the simultaneous estimation problem for the normal means. We set up the model structure using the several different distributions of the errors for observing their effects of model perturbation for the error terms in obtaining the empirical Bayes and hierarchical Bayes estimators. We compare the performance of those estimators under model perturbation based on a simulation study.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1119-1128
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• 2005

The Bayes factors with improper noninformative priors are defined only up to arbitrary constants. So it is known that Bayes factors are not well defined due to this arbitrariness in Bayesian hypothesis testing and model selections. The intrinsic Bayes factor and the fractional Bayes factor have been used to overcome this problem. In this paper, we suggest a Bayesian hypothesis testing based on the intrinsic Bayes factor and the fractional Bayes factor for the comparison of two lognormal variances. Using the proposed two Bayes factors, we demonstrate our results with some examples.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.227-235
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• 1998

In this paper, we consider a hierarchical Bayes estimation of the parameter, the reliability and hazard rate function based on type-II censored samples from a Rayleigh failure model. Bayes calculations can be implemented easily by means of the Gibbs sampler. A numerical study is provided.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1329-1341
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• 2006

When X and Y have independent gamma distributions, we consider the testing problem for two gamma means. We propose a solution based on a Bayesian model selection procedure to this problem in which no subjective input is considered. The reference prior is derived. Using the derived reference prior, we compute the fractional Bayes factor and the intrinsic Bayes factors. The posterior probability of each model is used as a model selection tool. Simulation study and a real data example are provided.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.155-162
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• 1999

This paper is concerned with the empirical Bayes estimation of one of the two shape parameters(${\theta}$) in the Burr(${\beta},\;{\theta}$) type XII failure model based on type-II censored data. We obtain the bootstrap empirical Bayes confidence intervals of ${\theta}$ by the parametric bootstrap introduced by Laird and Louis(1987). The comparisons among the bootstrap and the naive empirical Bayes confidence intervals through Monte Carlo study are also presented.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.885-893
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• 2013

This paper considers Bayes estimations of the small area means under Fay-Herriot model with measurement errors. We provide empirical Bayes predictors of small area means with the corresponding jackknifed mean squared prediction errors. Also we obtain hierarchical Bayes predictors and the corresponding posterior standard deviations using Gibbs sampling. Numerical studies are provided to illustrate our methods and compare their eciencies.