• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.777-786
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• 2001

The autoregressive models have been used to describe a wade variety of time series. Then the problem of determining the order in the times series model is very important in data analysis. We consider the Bayesian approach for finding the order of autoregressive(AR) error models using the latent variable which is motivated by Tanner and Wong(1987). The latent variables are combined with the coefficient parameters and the sequential steps are proposed to set up the prior of the latent variables. Markov chain Monte Carlo method(Gibbs sampler and Metropolis-Hasting algorithm) is used in order to overcome the difficulties of Bayesian computations. Three examples including AR(3) error model are presented to illustrate our proposed methodology.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.407-420
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• 1998

This paper is concerned with selecting covariates to be included in building linear random effects models designed to analyze clustered response normal data. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting premising subsets of covariates. The approach reformulates the linear random effects model in a hierarchical normal and point mass mixture model by introducing a set of latent variables that will be used to identify subset choices. The hierarchical model is flexible to easily accommodate sign constraints in the number of regression coefficients. Utilizing Gibbs sampler, the appropriate posterior probability of each subset of covariates is obtained. Thus, In this procedure, the most promising subset of covariates can be identified as that with highest posterior probability. The procedure is illustrated through a simulation study.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2009.11a
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• pp.459-468
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• 2009

Analysis of the uncertainty to have engineering solution of gas-dynamic and hydrodynamic problems is based on the comparison the prospective engineering solution with experimental result. In this paper, the mathematical model to estimate heat flux along gas-dynamic channel wall and the solution sequence are shown. Statistical information and generalizing experimental characteristics about gas- and hydro-dynamic channels were applied to the mathematical model. As the results, it is possible to draw a conclusion that models of the integrated approach, using the averaged statistical data of generalizing characteristics for a turbulent flow, without consideration of the turbulent mechanism (characteristic pulsations), can predict a nominal operating regime for gas-dynamic and hydrodynamic systems. The probable deviation of operating regime for newly designed the gas-dynamic channel can achieve 20% from a regime predicted on a basis 1-D or 3-D modelling irrespective of a kind of used models.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.10
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• pp.604-614
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• 2005

Color segmentation takes great attentions since a color is an effective and robust visual cue for characterizing one object from other objects. Color segmentation is, however, suffered from color variation induced from irregular illumination changes. This paper proposes a reliable color modeling approach in HSI (Hue-Saturation-Intensity) rotor space considering intensity information by adopting B-spline curve fitting to make a mathematical model for statistical characteristics of a color with respect to brightness. It is based on the fact that color distribution of a single-colored object is not invariant with respect to brightness variations even in HS (Hue-Saturation) plane. The proposed approach is applied for the segmentation of human skin areas successfully under various illumination conditions.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.149-154
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• 2001

Ship owners are demanding quieter vessels since crews have become more sensitive to their acoustic environment. Accordingly, designers of shipyards need to respond intelligently to the challenging requirements of delivering a quiet vessel. In early design stage, to predict shipboard noise the statistical approach is preferred to other methods because of simplicity. However, since the noise characteristics of the ships vary continuously with the environments, it is necessary to update the prediction formula with data base management system. This paper describes the feature of database program with the prediction method. Database management programs with GUI, are applied to Intranet system that is accessible by any users. Statistical approach to the prediction of A-weighted noise level in ship cabins, based on multiple regression analysis, is conducted. The noise levels in ship cabins are mainly affected by the parameters of the deadweight, the type of ship, the relative location of engines and cabins, the type of deckhouse, etc. As a result of verification, the formulas ensure the accuracy of 3 ㏈ in 83 % of cabins.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.67-80
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• 2017

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.397-419
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• 2017

A four-parameter extended fatigue lifetime model called the odd Birnbaum-Saunders geometric distribution is proposed. This model extends the odd Birnbaum-Saunders and Birnbaum-Saunders distributions. We derive some properties of the new distribution that include expressions for the ordinary moments and generating and quantile functions. The method of maximum likelihood and a Bayesian approach are adopted to estimate the model parameters; in addition, various simulations are performed for different parameter settings and sample sizes. We propose two new models with a cure rate called the odd Birnbaum-Saunders mixture and odd Birnbaum-Saunders geometric models by assuming that the number of competing causes for the event of interest has a geometric distribution. The applicability of the new models are illustrated by means of ethylene data and melanoma data with cure fraction.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.55-62
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• 2006

Huang proposed a grey-based nearest neighbor approach to predict accurately missing attribute value in 2004. Our study proposes which way to decide the number of nearest neighbors using not only the dong's grey relational grade but also the wen's grey relational grade. Besides, our study uses not an arithmetic(unweighted) mean but a weighted one. Also, GRG is used by a weighted value when we impute a missing values. There are four different methods - DU, DW, WU, WW. The performance of WW(wen's GRG & weighted mean) method is the best of my other methods. It had been proven by Huang that his method was much better than mean imputation method and multiple imputation method. The performance of our study is far superior to that of Huang.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.191-194
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• 2001

In general, the imputation problems which are caused from survey nonresponse have been studied for being based on ignorable cases. However the model based approach can be applied to survey with nonresponse suspected of being nonignorable. Here in this study, we will make the nonresponse for nonignorable into ignorable cell using adjustment cell approach, then we can applied the ignorable nonresponse method. For data sets of each nonresponse cells are simulated from normal distribution.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.191-204
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• 2006

This paper develops a Bayesian method to derive the optimal sequential preventive maintenance(PM) policy by determining the PM schedules which minimize the mean cost rate. Such PM schedules are derived based on a general sequential imperfect PM model proposed by Lin, Zuo and Yam(2000) and may have unequal length of PM intervals. To apply the Bayesian approach in this problem, we assume that the failure times follow a Weibull distribution and consider some appropriate prior distributions for the scale and shape parameters of the Weibull model. The solution is proved to be finite and unique under some mild conditions. Numerical examples for the proposed optimal sequential PM policy are presented for illustrative purposes.