• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.165-172
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• 1998

This paper considers a linear regression model with censored data where each error term follows a multivariate normal distribution. In this paper we consider the diffuse prior distribution for parameters of the linear regression model. With censored data we derive the full conditional densities for parameters of a multiple regression model in order to obtain the marginal posterior densities of the relevant parameters through the Gibbs Sampler, which was proposed by Geman and Geman(1984) and utilized by Gelfand and Smith(1990) with statistical viewpoint.

• Journal of the Korean Statistical Society
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• 제28권4호
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• pp.489-501
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• 1999

In this paper, we consider the panel data regression model in which the disturbances have nested error component. We derive a Lagrange Multiplier(LM) test which is jointly testing for the presence of random individual effects and nested effects under the normality assumption of the disturbances. This test extends the earlier work of Breusch and Pagan(1980) and Baltagi and Li(1991). Further, it is shown that this LM test has the same asymptotic distribution without normality assumption of the disturbances.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.121-131
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• 2007

In this paper, we examine the problem of estimating the sensitive characteristics and behaviors in a multinomial randomized response model using Bayesian approach. We derived a posterior distribution for parameter of interest for multinomial randomized response model. Based on the posterior distribution, we also calculated a credible intervals and mean squared error (MSE). We finally compare the maximum likelihood estimator and the Bayes estimator in terms of MSE.

• Journal of the Korean Data and Information Science Society
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• 제19권3호
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• pp.951-957
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• 2008

We derive some approximate maximum likelihood estimators(AMLEs) and maximum likelihood estimator(MLE) of the scale parameter in the half-triangle distribution based on progressively Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples. We also obtain the approximate maximum likelihood estimators of the reliability function using the proposed estimators. We compare the proposed estimators in the sense of the mean squared error.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.629-638
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• 2005

We derive the approximate maximum likelihood estimators of the scale parameter and location parameter of the extreme value distribution based on multiply Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples.

• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.209-217
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• 2001

We obtain the approximate maximum likelihood estimators (AMLEs) for the scale and location parameters $\theta$ and $\mu$ in the three-parameter Weibull distribution based on Type-II censored samples. We also compare the AMLEs with the modified maximum likelihood estimators (MMLEs) in the sense of the mean squared error (MSE) based on complete sample.

• 한국통신학회논문지
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• 제14권6호
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• pp.590-603
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• 1989

본 논문에서는 단축 Hamming 부호의 일종이며 오류검출용 검사비트 수가 16인 CRC-CCITT 부호화 원시다항식 CRC 부호에 대한 성능 분석을 위하여 필수적으로 요구되는 중분포(weight distribution)를 구하는 기법과 오류검출 성능을 분석하는 기법을 제안하였고, 두 CRC(cyclic redundant code)부호를 CCITT에서 광대역 ISDN의 가입자망 인터페이스의 전송방식으로 권고된 ATM(asynchronous transfer mode)전송방식의 오류검출을 부호로 적용하여 현재 고려되고 있는 cell 크기에 대한 증분포 및 미검출오류확률(undetected error probability)을 구한 후, 두 오류검출부호의 성능을 비교/분석 하였다. 분석 결과, 현재 고려되는 셀 크기에 대해 CRC-CCITT 부호의 성능이 원시다항식 CRC 부호의 성능보다 더 우수함이 입증되었다 .이를 위한 모든 계산을 IBM PC/AT를 이용하여 수행하였다. 한편 본 논문에서 제안한 단축 Hamming 부호의 성능 분석 기법은 지금까지 디지틀 통신시스템에 적용되고 있는 또는 적용예정인 CRC 오류검출 부호의 성능 분석에 이용될 수 있다.

• Structural Engineering and Mechanics
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• 제81권1호
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• pp.103-115
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• 2022

The prediction error variances for frequencies are usually considered as unknown in the Bayesian system identification process. However, the error variances for mode shapes are taken as known to reduce the dimension of an identification problem. The present study attempts to explore the effectiveness of Bayesian approach of model parameters updating using Markov Chain Monte Carlo (MCMC) technique considering the prediction error variances for both the frequencies and mode shapes. To remove the ergodicity of Markov Chain, the posterior distribution is obtained by Gaussian Random walk over the proposal distribution. The prior distributions of prediction error variances of modal evidences are implemented through inverse gamma distribution to assess the effectiveness of estimation of posterior values of model parameters. The issue of incomplete data that makes the problem ill-conditioned and the associated singularity problem is prudently dealt in by adopting a regularization technique. The proposed approach is demonstrated numerically by considering an eight-storey frame model with both complete and incomplete modal data sets. Further, to study the effectiveness of the proposed approach, a comparative study with regard to accuracy and computational efficacy of the proposed approach is made with the Sequential Monte Carlo approach of model parameter updating.

• 전자공학회논문지SC
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• 제44권3호
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• pp.45-50
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• 2007

1550nm의 파장대를 이용하는 자동 위상보정 양자암호 시스템을 소개한다. 양자키 분배 시스템에서 자동위상보정된 양자키 분배를 위한 메인 컨트롤보드와 phase modulator를 제어할 수 있는 보드를 제작하였고, 단일광자검출기를 위한 dark count당 photon count, quantum key distribution rate($R_{sift}$)와 quantum bit error rate(QBER)값을 측정하였다. 이 시스템은 25km의 광섬유상에서 quantum bit error rate(QBER) 3.5%의 결과값을 얻었고, 이는 상용화가 가능할 것으로 예상된다.

• Communications for Statistical Applications and Methods
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• 제15권2호
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• pp.161-171
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• 2008

This study discusses Johnson's $S_U$-normal distribution capturing a wide range of non-normality in various regression models. We provide the likelihood inference using Johnson's $S_U$-normal distribution, and propose a likelihood ratio (LR) test for normality. We also apply the $S_U$-normal distribution to the binary and censored regression models. Monte Carlo simulations are used to show that the LR test using the $S_U$-normal distribution can be served as a model specification test for normal error distribution, and that the $S_U$-normal maximum likelihood (ML) estimators tend to yield more reliable marginal effect estimates in the binary and censored model when the error distributions are non-normal.