• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.551-560
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• 2010

This paper considers a Bayesian approach to modeling a flexible regression function under structural measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under structural measurement error model without a semiparametric component.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1941-1945
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• 2011

In this paper, we propose a real-time classifier to distinguish humans from livestock by using the spatial integral. The image-difference method and the Expectation Maximization are used to reduce noises in input image. A histogram analysis based on Simulated Annealing and the fuzzy-Bayesian algorithm are used to classify human and livestock. Finally, the experiment results show the validity of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.543-548
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• 2007

We present a dynamic Bayesian network (DBN) model of a generalized class of nonstationary birth-death processes. The model includes birth and death rate parameters that are randomly selected from a known discrete set of values. We present an on-line algorithm to obtain optimal estimates of the parameters. We provide a simulation of real-time characterization of load traffic estimation using our DBN approach.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.733-742
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• 1998

For industrial and medical lifetime data, the generalized log-gamma regression model is considered. Then the Bayesian analysis for the generalized log-gamma regression with censored data are explained and following the data augmentation (Tanner and Wang; 1987), the censored data is replaced by simulated data. To overcome the complicated Bayesian computation, Makov Chain Monte Carlo (MCMC) method is employed. Then some modified algorithms are proposed to implement MCMC. Finally, one example is presented.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.1023-1030
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• 2003

When X and Y have independent normal distributions, we develop a Bayesian testing procedure for the equality of two coefficients of variation. Under the reference prior of the coefficient of variation, we propose a Bayesian test procedure for the equality of two coefficients of variation using fractional Bayes factor. A real data example is provided.

• 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.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.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1037-1040
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• 1999

This paper investigates a RBF(Radial Basis Function) equalizer for channel equalization. RBF network has an identical structure to the optimal Bayesian symbol-decision equalizer solution. Therefore RBF can be employed to implement the Bayesian equalizer. Proposed algorithm of this paper makes channel states estimation to be unncessary, also makes center number which is needed indivisual channel to be minimum. Bayesian Equalizer has the theorical optimum performance. Proposed Equalizer performance is compared with this Baysian equalizer performance.

• Journal of Korean Society for Quality Management
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• v.34 no.1
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• pp.48-53
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• 2006

Applying Bayesian optimal design principles is not easy when a prior distribution is not certain. We present a optimal design criterion which possibly yield a reasonably good design and also robust with respect to misspecification of the prior distributions. The criterion is applied to the problem of estimating the turning point of a quadratic regression. Exact mathematical results are presented under certain conditions on prior distributions. Computational results are given for some cases not satisfying our conditions.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.183-189
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• 2003

A momentum threshold autoregressive (MTAR) model, a nonlinear autoregressive model, is analyzed in a Bayesian framework. Parameter estimation in the presence of missing data is done by using Markov chain Monte Carlo methods. We also propose simple Bayesian test procedures for asymmetry and unit roots. The proposed method is applied to a set of Korea unemployment rate data and reveals evidence for asymmetry and a unit root.