• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.381-387
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• 2011

This paper suggests a method for estimating components of variance in one-factor random model. Estimates of variance components are given by the method of moments. Sums of squares due to variance sources are obtained by projections. This paper also shows how to use eigenvalues for getting the coefficients of variance components in the expression of the expectations of the mean squares. The suggested method shows easier and faster than the method of Harley's synthesis.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.303-310
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• 2004

We suggest a method to procure information from the sensitive population which combine a direct survey method, BB and an indirect survey one, RRT, and a combined estimator that uses the stratified double sampling to estimate the sensitive parameter. We compare the efficiency of our estimator with that of Mangat and Singh model.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.325-327
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• 2001

본 논문은 온라인 문자 인식을 연속 밀도 HMM의 구조의 최적화 문제를 다룬다. 최적이란 최소한의 모델 파라미터를 사용하여 최소한의 오류를 허용하는 것이라고 정의할 수 있다. 본 연구에서는 HMM 구조의 최적화를 위해 Bayesian 모델 선택 방법론을 사용한다. 먼저 잘 알려진 BIC(Bayesian Information Criterion)을 적용해보고, 그것을 HMM의 복잡한 구조에 적합하도록 본 논문에서 제안한 HBIC(HMM-Oriented BIC)와 비교해본다. BIC는 모델의 사전 확률 분포를 추정하지 않고 다변량 정규분포라고 가정하는데 비해 HBIC는 모델의 각 파라미터로부터 사전 확률을 추정한 후 그것들을 사용함으로써 더 좋은 결과를 얻도록 한다. 실험 결과 BIC와 HBIC 둘 다 기존 방법보다 모델의 파라미터 수를 현저히 감소시킴을 확인했고, HBIC가 BIC에 비해 더 적은 수의 파라미터를 사용해도 비슷한 인식률을 얻을 수 있었다.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.161-174
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• 2000

Bayesian inversion is a stable approach to infer the subsurface structure with the limited data from geophysical explorations. In geophysical inverse process, due to the finite and discrete characteristics of field data and modeling process, some uncertainties are inherent and therefore probabilistic approach to the geophysical inversion is required. Bayesian framework provides theoretical base for the confidency and uncertainty analysis for the inference. However, most of the Bayesian inversion require the integration process of high dimension, so massive calculations like a Monte Carlo integration is demanded to solve it. This method, though, seemed suitable to apply to the geophysical problems which have the characteristics of highly non-linearity, we are faced to meet the promptness and convenience in field process. In this study, by the Gaussian approximation for the observed data and a priori information, fast Bayesian inversion scheme is developed and applied to the model problem with electric well logging and dipole-dipole resistivity data. Each covariance matrices are induced by geostatistical method and optimization technique resulted in maximum a posteriori information. Especially a priori information is evaluated by the cross-validation technique. And the uncertainty analysis was performed to interpret the resistivity structure by simulation of a posteriori covariance matrix.

• Geophysics and Geophysical Exploration
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• v.10 no.4
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• pp.293-298
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• 2007

A new way to integrate various geophysical information for evaluation of RQD was developed. In this study, we does not directly define the RQD value where borehole data are not sampled. Instead, we infer the probability of RQD values with prior probability of data directly obtained from borehole, and secondary supporting probability from resistivity and seismic tomography data. First, we applied the geostatstical indicator kriging to get prior probability of RQD value, and indicator kriging with soft data to get the supporting probability from resistivity and seismic data. And we finally applied the permanence ratio rule to integrate these information. The finally obtained result was also analyzed to fully utilize the probabilistic features. For example, we showed the probability of wrongly classifying the RQD evaluation and vice versa. This kind of analytical result may be used for decision making process based on the geophysical exploration.

• Journal of agriculture & life science
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• v.45 no.6
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• pp.279-289
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• 2011

The purpose of this study is to estimate efficiency of item-based agricultural cooperative by using Data Envelopment Analysis. A proposed method employs a bootstrapping approach to generating efficiency estimates through Monte Carlo simulation resampling process. The technical efficiency, pure technical efficiency, and scale efficiency measure of item-based agricultural cooperative is 0.80, 0.87, 0.93 respectively. However the bias-corrected estimates are less than those of DEA. We know that the DEA estimator is an upward biased estimator. In technical efficiency, average lower and upper confidence bounds of 0.726 and 0.8747. According to these results, the DEA bootstrapping model used here provides bias-corrected and confidence intervals for the point estimates, it is more preferable.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.43-47
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• 2005

확률밀도함수가 명확히 표현되지 않고 오직 백분위함수로만 표현되는 분포에서 최우추정치를 구하는 수치적 최적화 알고리즘에 대해서 연구하였다. 이 최우추정 알고리즘을 수문학 등에서 사용되는 5-모수의 웨이크비 분포에 적용하였으며, 몬테카를로 시뮬레이션을 통하여 L-적률추정법과 그 성능을 비교하였다.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.181-193
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• 2003

Without a sample standard deviation for an estimator of the population standard deviation u in a sample size computations, we often use some functions of a sample range (R) or interquartile range (IQR) by an estimator of $\sigma$. In order to avoid under-powered studies, these estimates must have a high probability of being greater than or equal to $\sigma$. In this paper, these probabilities of being greater than or equal to $\sigma$ are estimated for IQR for various parents distributions, and are compared with the probabilities for R/4 (Browne 2001). Alternative divisors (K) are explored and discussed for which the probabilities of R/K and IQR/K being greater than or equal to $\sigma$ is at least 95%.

• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.141-157
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• 2007

This paper evaluates the effects of privatization and deregulation on the firm-specific efficiency and total factor productivity (TFP) growth in the Korean and Japanese railways. Using a stochastic frontier approach and a generalized translog functional form, the paper specifies the equation system consisting of a multiproduct variable cost function and input share equations which is estimated with Zellner's iterative seemingly unrelated regression and the corrected least squares method. The Korean and Japanese railway firms are assumed to produce three outputs (Shinkansen passenger-kilometers, incumbent railway passenger-kilometers, ton-kilometers of freight) using three input factors (labor, fuel, maintenance and rolling stock). A monetary value of the ways and fixed installations held by the railroad firm is also included as a quasi-fixed input. The empirical results indicate that the average estimate of cost inefficiency is 2.57% for the total sample and on the average, JNR and JR Kyushu are found to be worst efficient while the most efficient railway firm in the sample is JR West. Also the cost efficiency levels of seven JRs have been improved after the reform and privatization of JNR. The findings also indicate that TFP growth of the privately-owned JRs are higher than those of the government-owned KNR and JNR. Three-island JRs and JR Freight have slightly higher TFP growth than Honshu JRs as well. Thus, the results suggest that managerial autonomy and increased competition via deregulation have improved efficiency and TFP growth.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1299-1306
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• 2011

In this study, crack-growth model parameters subjected to variable amplitude loading are estimated in the form of a probability distribution using the method of Bayesian parameter estimation. Huang's model is employed to describe the retardation and acceleration of the crack growth during the loadings. The Markov Chain Monte Carlo (MCMC) method is used to obtain samples of the parameters following the probability distribution. As the conventional MCMC method often fails to converge to the equilibrium distribution because of the increased complexity of the model under variable amplitude loading, an improved MCMC method is introduced to overcome this shortcoming, in which a marginal (PDF) is employed as a proposal density function. The model parameters are estimated on the basis of the data from several test specimens subjected to constant amplitude loading. The prediction is then made under variable amplitude loading for the same specimen, and validated by the ground-truth data using the estimated parameters.