• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.845-852
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• 2010

The outcomes of counts commonly occur in the area of disease mapping for mortality rates or disease rates. A Poisson distribution is usually assumed as a model of disease rates in conjunction with a gamma prior. The small area typically refers to a small geographical area or demographic group for which very little information is available from the sample surveys. Under this situation the model-based estimation is very popular, in which the auxiliary variables from various administrative sources are used. The empirical Bayes estimator under Poissongamma model has been considered with its accuracy measures. An accuracy measure using a bootstrap samples adjust the underestimation incurred by the posterior variance as an estimator of true mean squared error. We explain the suggested method through a practical dataset of hitters in baseball games. We also perform a Monte Carlo study to compare the accuracy measures of mean squared error.

• Journal of Korean Society for Quality Management
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• v.42 no.4
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• pp.747-756
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• 2014

Purpose: The purpose of this study is to apply the Bayesian estimation methodology for producing 'Korean Standard -Quality Excellence Index' model and prove the effectiveness of the new approach based on survey data by comparing the current index with the new index produced by Bayesian estimation method. Methods: The 'Korean Standard -Quality Excellence Index' was produced through the collected survey data by Bayesian estimation method and comparing the deviation with two results for confirming the effectiveness of suggested application. Results: The statistical analysis result shows that suggested estimator, that is, empirical Bayes estimator improves the effectiveness of the index with regard to reduce the error under specific loss function, which is suggested for checking the goodness of fit. Conclusion: Considering the Bayesian techniques such as empirical Bayes estimator for producing the quality excellence index reduces the error for estimating the parameter of interest and furthermore various Bayesian perspective approaches seems to be meaningful for producing the corresponding index.

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.45-56
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• 1995

The method of lot by lot quality protection for sampling inspection by variables is widely used in quality control. In case of sampling inspection being done repeatedly, one can use the information from the previous sampling inspection to improve current estimates. This article shows that empirical Bayes estimator is superior to the usual sample mean in repeated sampling inspection by variables.

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.551-572
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• 2005

Recently, there are some empirical Bayes procedures using NA samples. We point out a key equality which may not hold for NA samples. Thus, the results of those empirical Bayes procedures based on NA samples are dubious

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.515-520
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• 2002

In general, we use the hierarchical Poisson-gamma model for the Poisson data in generalized linear model. Time effect will be emphasized for the analysis of the observed data to be collected annually for the time period. An extended model with time effect for estimating the effect is proposed. In particularly, we discuss the Quasi likelihood function which is used to numerical approximation for the likelihood function of the parameter.

• Journal of Korean Society for Quality Management
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• v.19 no.2
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• pp.86-95
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• 1991

Empirical Bayes estimation of the binomial parameter ${\theta}$ is considered when the sample size in each component problem is random. The result is applied to the lot acceptance sampling problem in the presence of various kinds of costs including cost for sampling.

• Journal of the Korean Statistical Society
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• v.16 no.2
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• pp.92-101
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• 1987

Our interest is to access in some way teh relative odds or probability that a multivariate observation Z belongs to one of k multivariate normal populations with unequal covariance matrices. We derived the empirical Bayes posterior odds ratio for the classification rule when population parameters are unknown. It is a generalization of the posterior odds ratio suggested by Gelsser (1964). The classification rule does not have complicated distribution theory which a large variety of techniques from the sampling viewpoint have. The proposed posterior odds ratio is compared to the Gelsser's posterior odds ratio through a Monte Carlo study. The results show that the empiricla Bayes posterior odds ratio, in general, performs better than the Gelsser's. Especially, for large dimension of Z and small training sample, the performance is prominent.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.221-235
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• 1999

Let be $X_1$,...,$X_p$, $p\geq2$ independent random variables where each $X_i$ has a gamma distribution with $\textit{k}_i$ and $\theta_i$ The problem is to simultaneously estimate $\textit{p}$ gamma parameters $\theta_i$ and $\theta_i{^-1}$ under entropy loss where the parameters are believed priori. Hierarch ical Bayes(HB) and empirical Bayes(EB) estimators are investigated. And a preference of HB estimator over EB estimator is shown using Gibbs sampler(Gelfand and Smith 1990). Finally computer simulation is studied to compute the risk percentage improvements of the HB estimator and the estimator of Dey Ghosh and Srinivasan(1987) compared to UMVUE estimator of $\theta^{-1}$.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.119-128
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• 2002

In this paper, we consider the HB estimators of small area means with repeated survey. mao and Yu(1994) considered small area model with repeated survey data and proposed empirical best linear unbiased estimators. We propose a hierachical Bayes version of Rao and Yu by assigning prior distributions for unknown hyperparameters. We illustrate our HB estimator using very popular data in small area problem and then compare the results with the estimator of Census Bureau and other estimators previously proposed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.46-54
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• 2009

This study deals with the effects on the accident reduction according to the installation of RLC (red light cameras). The objective is to analyze the effects on the accident reduction using EB (Empirical Bayes) method. In pursuing the above, the study uses the 728 accident data occurred at the 28 intersections which RLC are installed. The main results are as follows. First, the effects of accident reduction were analyzed to be 20.74% by simple before-after study method. Second, the safety performance functions (SPF) were developed by the Poisson and negative binominal regression models, and since the over-dispersion parameter was close to zero, Poisson model was evaluated to be more appropriate than the negative binominal model. Also, the Poisson model was analyzed to be statistically significant because its ${\rho}^2$ value was 0.409. Finally, the results of analysis using an EB method showed that the accidents were reduced by range from 3.89 to 29.23%.