• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.181-191
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• 1999

We consider Bayesian models allow multiple grouping of parameters for the normal means estimation problem. In particular, we consider a typical Bayesian hierarchical approach based on thepartial exchangeability where the components within a subgroup are exchangeable, but the different subgroups are not. We discuss implementation of such Bayesian procedures via Gibbs sampling. We illustrate the proposed methods with numerical examples.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.501-510
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• 1999

In this paper, we consider a comparable study on three Bayes procedures for the multivariate normal mean estimation problem. In specific we consider hierarchical Bayes empirical Bayes and robust Bayes estimators for the normal means. Then three procedures are compared in terms of the four comparison criteria(i.e. Average Relative Bias (ARB) Average Squared Relative Bias (ASRB) Average Absolute Bias(AAB) Average Squared Deviation (ASD) using the real data set.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.323-327
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• 2004

In this paper, we investigate the limiting distribution of the Ljung-Box test statistic in the unit root AR models with ARCH errors. We show that the limiting distribution is approximately chi-square distribution with the degrees of freedom only depending on the number of autocorrelation lags appearing in the test. Some simulation results are provided for illustration.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.11-19
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• 1997

One of the most important issues in regression is variable selection problem. Recently several methods have been proposed to overcome the overparameterization property of Mallow's $C_p$. In this paper we compare these model selection criteria in view of the performance of selecting true model by simulation study.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.193-205
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• 1997

The multiprocess dynamic model provides a good framework for the modeling and analysis of the time series that contains outliers and is subject to abrupt changes in pattern. In this paper we consider the multiprocess discount generalized model with parameters having a dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt change of pattern in parameters.

• Journal of the Korean Data and Information Science Society
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• v.6 no.2
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• pp.29-39
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• 1995

A model used in the past may be altered at will in modeling for the future. For this situation, the multiprocess dynamic model provides a general framework. In this paper we consider the multiprocess discount normal model with parameters having a time dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt changes of pattern.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.219-228
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• 2007

In this paper, we consider a decision-theoretic oriented, objective Bayesian inference for the ratio of two normal variances. Specifically we derive the Bayesian reference criterion as well as the intrinsic estimator and the credible region which correspond to the intrinsic discrepancy loss and the reference prior. We illustrate our results using real data analysis and simulation study.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.49-58
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• 1997

We consider the quantile function for the bootstrapped product limit estimate under left truncation and right censoring model and show its weak convergence. We also obtain bootstrapped confidence bands for the quantile function.

• Journal of the Korean Data and Information Science Society
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• v.3 no.1
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• pp.79-90
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• 1992

We consider hazard ratio as a descriptive measure to compare the hazard experience of a treatment group with that of a control group with censored survival data. In this paper, we propose a kernel estimator of hazard ratio. The uniform consistency and asymptotic normality of a kernel estimator are proved by using counting process approach via martingale theory and stochastic integrals.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.1033-1043
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• 2004

We consider the simultaneous Bayesian estimation for the normal means based on different noninformative type hyperpriors in hierarchical model. We provide numerical example using the famous baseball data in Efron and Morris (1975) for illustration.