• 응용통계연구
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• 제24권2호
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• pp.383-391
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• 2011

이 논문에서는 역가우스분포의 적합을 위한 변형된 엔트로피 기반 검정을 제시한다. 이 검정은 자료생성분포와 역가우스분포의 엔트로피 차이에 기초를 두고 있으며 검정통계량은 엔트로피 차이의 추정량을 사용한다. 엔트로피 차이의 추정량은 자료생성분포에 대한 엔트로피 추정량으로 Vasicek의 표본엔트로피와 역가우스분포에 대한 엔트로피 추정량로 균일최소분산불편추정량을 사용하여 얻는다. 모의실험을 통해 얻은 표본크기와 윈도크기에 따른 검정통계량의 기각값들을 표의 형태로 제공한다. 제안한 검정의 검정력 알아보기 위해 여러 대립분포와 표본크기에 대해서 모의실험을 수행하고 기존의 엔트로피 기반 검정과 비교한다.

• 응용통계연구
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• 제27권4호
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• pp.605-617
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• 2014

소지역추정은 작은 규모의 지역 또는 도메인에 작은 크기의 표본이 배정되어 추정의 정도가 좋지 않은 경우에 이를 극복하는 통계적 기법이다. 소지역추정에 흔히 사용되고 있는 모형기반 추정량은 MSE를 기초로 얻어지나 최근 상대오차를 이용한 소지역추정법도 연구되고 있다. 본 논문에서는 상대오차를 최소로 하는 소지역 추정량의 준모수적 접근법에 관하여 연구하였다. 즉 준모수혼합모형을 이용한 축소소지역추정량을 새롭게 제안하였다. 또한 Lee(1995)에서 제안된 모의실험 자료를 이용한 모의실험과 매월노동통계 자료를 이용한 사례연구를 통하여 기존의 추정량과 제안된 추정량의 우수성을 비교하였다.

• 대한전기학회논문지:전력기술부문A
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• 제48권8호
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• pp.996-1003
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• 1999

This paper propose a position-speed controller with an estimator which can estimate states and disturbance. The overall control system consists of two parts: the position-speed controller and an estimator. The Kalman filter applied as state-feedback controller is an optimal state estimator applied to a dynamic system that involves random perturbations and gives a linear, unbiased and minimum error variance recursive algorithm to optimally estimate the unknown state. Therefore, we consider the error problem about the servo system modeling and the measurement noise as a stochastic system and implement a optimal state observer, and enhance the estimate performance of position and speed using that. Using two-degree-of freedom(TDOF) conception, we design the command input response and the closed loop characteristics independently. The servo system is to improve the closed loop characteristics without affecting the command imput response. The characteristics of the closed loop system is improved by suppressing disturbance torque effectively with the disturbance observer using a inverse-transfer matrix. Therefore, the performance of overall position-speed controller is enhanced. Finally, the performance of the proposed controller is exemplified by some simulations and by applying the real servo system.

• Structural Monitoring and Maintenance
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• 제6권4호
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• pp.317-346
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• 2019

Performance assessment of pavements proves useful, in terms of handling the ride quality, controlling the travel time of vehicles and adequate maintenance of pavements. Roughness profiles provide a good measure of the deteriorating condition of the pavement. For the accurate estimates of pavement roughness from dynamic vehicle responses, vehicle parameters should be known accurately. Information on vehicle parameters is uncertain, due to the wear and tear over time. Hence, condition monitoring of pavement requires the identification of pavement roughness along with vehicle parameters. The present study proposes a scheme which estimates the roughness profile of the pavement with the use of accurate estimates of vehicle parameters computed in parallel. Pavement model used in this study is a two-layer Euler-Bernoulli beam resting on a nonlinear Pasternak foundation. The asphalt topping of the pavement in the top layer is modeled as viscoelastic, and the base course bottom layer is modeled as elastic. The viscoelastic response of the top layer is modeled with the help of the Burgers model. The vehicle model considered in this study is a half car model, fitted with accelerometers at specified points. The identification of the coupled system of vehicle-pavement interaction employs a coupled scheme of an unbiased minimum variance estimator and an optimization scheme. The partitioning of observed noisy quantities to be used in the two schemes is investigated in detail before the analysis. The unbiased minimum variance estimator (MVE) make use of a linear state-space formulation including roughness, to overcome the linearization difficulties as in conventional nonlinear filters. MVE gives estimates for the unknown input and fed into the optimization scheme to yield estimates of vehicle parameters. The issue of ill-posedness of the problem is dealt with by introducing a regularization equivalent term in the objective function, specifically where a large number of parameters are to be estimated. Effect of different objective functions is also studied. The outcome of this research is an overall measure of pavement condition.

• 품질경영학회지
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• 제9권1호
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• pp.3-11
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• 1981

동일한 부품 K개를 갖고 있으며, 그 중에서 S개 이상의 스트렝스(strength)가 스트레스(stress) 보다 크게 될 경우 신뢰성이 유지되는 시스템에서 스트레스와 스트렝스가 모두 와이블(weibull) 분포를 하고 있을 때의 시스템 신뢰성을 고찰하였다. 2 절에서는 시스템 신뢰성의 최소분산불편추정량(MVU estimator)을 구하였고, 3 절에서는 최소분산불편추정량의 점근분포(asymototic distribution)를 구하고 표본크기가 클때 시스템 신뢰성의 최소분산불편추정량과 최우추정량(MLE)과의 관계를 구하였으며, 4 절에서는 시스템 신뢰성의 일양최적불편신뢰구간(uniformly most accurate unbiased confidence interval) 을 구하였고, 5 절에서는 몬데 카를로 씨뮤레이션(Monte Carlo Simulation)을 사용하여 작은 표본에서의 최우추정량과 최소분산불편추정량의 편기(bias)와 평균자승오차(MSE)를 비교하였고 6 절에서는 결과를 간단히 요약하고 본 논문을 더 확장할 경우에 문제점을 제시하였다.

• 응용통계연구
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• 제22권1호
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• pp.153-161
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• 2009

We consider a logistic model with random effects as the superpopulation for estimating the small area pro-portions. The best linear unbiased predictor under linear mired model is popular in small area estimation. We use this type of estimator under logistic mixed motel for the small area proportions, on which the estimation of mean squared error is also discussed. Two kinds of estimation methods, the parametric bootstrap and the linear approximation will be compared through a Monte Carlo study in the respects of the normality assumption on the random effects distribution and also the magnitude of sample sizes on the approximation.

• Journal of the Korean Statistical Society
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• 제24권1호
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• pp.45-64
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• 1995

When a random sample is taken from a certain class of discrete and continuous distributions whose support depend on two parameters, we could find that there exists the complete and sufficient statistic for parameters which belong to a certain class, and fomulate the uniformly minimum variance unbiased estimator (UMVUE) of any estimable function. Some UMVUE's of parametric functions are illustrated for the class of the distribution. Especially, we find that the UMVUE of some estimable parametric function from the truncated normal distribution could be expressed by the version of the Mill's ratio.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.559-567
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• 2018

In this paper, we propose a new load forecasting method for smart air conditioning (A/C) based on the modified thermodynamics of indoor temperature and the unbiased finite memory estimator (UFME). Based on modified first-order thermodynamics, the dynamic behavior of indoor temperature can be described by the time-domain state-space model, and an accurate estimate of indoor temperature can be achieved by the proposed UFME. In addition, a reliable A/C load forecast can be obtained using the proposed method. Our study involves the experimental validation of the proposed A/C load forecasting method and communication construction between DR server and HEMS in a test bed. Through experimental data sets, the effectiveness of the proposed estimation method is validated.

• International Journal of Reliability and Applications
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• 제14권1호
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• pp.27-39
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• 2013

In this article, a new model based on the Rayleigh distribution is introduced. This model is useful and practical in physics, reliability, and life testing. The statistical and reliability properties of this model are presented, including moments, the hazard rate, the reversed hazard rate, and mean residual life functions, among others. In addition, it is shown that the distributions of the new model are ordered regarding the strongest likelihood ratio ordering. Four estimating methods, namely, method of moment, maximum likelihood method, Bayes estimation, and uniformly minimum variance unbiased, are used to estimate the parameters of this model. Simulation is used to calculate the estimates and to study their properties. Finally, the appropriateness of this model for real data sets is shown by using the chi-square goodness of fit test and the Kolmogorov-Smirnov statistic.

• 대한수학회지
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• 제61권3호
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• pp.427-444
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• 2024

The matrix completion problem is to predict missing entries of a data matrix using the low-rank approximation of the observed entries. Typical approaches to matrix completion problem often rely on thresholding the singular values of the data matrix. However, these approaches have some limitations. In particular, a discontinuity is present near the thresholding value, and the thresholding value must be manually selected. To overcome these difficulties, we propose a shrinkage and thresholding function that smoothly thresholds the singular values to obtain more accurate and robust estimation of the data matrix. Furthermore, the proposed function is differentiable so that the thresholding values can be adaptively calculated during the iterations using Stein unbiased risk estimate. The experimental results demonstrate that the proposed algorithm yields a more accurate estimation with a faster execution than other matrix completion algorithms in image inpainting problems.