• Journal of the Korean Statistical Society
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• 제36권4호
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• pp.543-556
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• 2007

In successive sampling on two occasions the problem of estimating a finite population quantile has been considered. The theory developed aims at providing the optimum estimates by combining (i) three double sampling estimators viz. ratio-type, product-type and regression-type, from the matched portion of the sample and (ii) a simple quantile based on a random sample from the unmatched portion of the sample on the second occasion. The approximate variance formulae of the suggested estimators have been obtained. Optimal matching fraction is discussed. A simulation study is carried out in order to compare the three estimators and direct estimator. It is found that the performance of the regression-type estimator is the best among all the estimators discussed here.

• 대한전자공학회논문지
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• 제18권2호
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• pp.22-26
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• 1981

실제로 제한된 대역이나 협대역의 random신호를 interpolate할 경우 sampling이론에 근거하여 유한한 항들을 취하는 truncated expansion은 매우 유용하다. 본 논문은 해양 시스템의 동적 분석에 있어 효과적이고도 통계학적으로 대확한algorithm을 얻는데 목적을 두고 있다. Truncated sampling expansion의 통계학적 정확도가 조사되어지고 간단한 해양 시스템의 예를 들어, 실제 wave data를 가지고, 정확도를 많이 향상시키면서도 계산면에서 거의 복잡성을 주지 않는 새로운 algorithm을 보여 준다.

• Structural Engineering and Mechanics
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• 제5권2호
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• pp.115-126
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• 1997

Importance sampling methods have been developed with the aim of reducing the computational costs inherent in Monte Carlo methods. This study proposes a new algorithm called the adaptive kernel method which combines and modifies some of the concepts from adaptive sampling and the simple kernel method to evaluate the structural reliability of time variant problems. The essence of the resulting algorithm is to select an appropriate starting point from which the importance sampling density can be generated efficiently. Numerical results show that the method is unbiased and substantially increases the efficiency over other methods.

• Communications for Statistical Applications and Methods
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• 제28권6호
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• pp.643-653
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• 2021

Few studies are found in literature on estimation of population quantiles using the method of ranked set sampling (RSS). The optimal RSS strategy is to select observations with at most two fixed rank order statistics from different ranked sets. In this paper, a near optimal unbalanced RSS model for estimating p^th(0 < p < 1) population quantile is proposed. Main advantage of this model is to use each rank order statistics and is distributionfree. The asymptotic relative efficiency (ARE) for balanced RSS, unbalanced optimal and proposed near-optimal methods are computed for different values of p. We also compared these AREs with respect to simple random sampling. The results show that proposed unbalanced RSS performs uniformly better than balanced RSS for all set sizes and is very close to the optimal RSS for large set sizes. For the practical utility, the near optimal unbalanced RSS is recommended for estimating the quantiles.

• Nuclear Engineering and Technology
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• 제54권1호
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• pp.414-422
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• 2022

Presented in this paper is a methodology for combining a Bayesian statistical approach with Data Quality Objectives (a structured decision-making method) to provide increased levels of confidence in analytical data when approaching a waste boundary. Development of sampling and analysis plans for the characterisation of radioactive waste often use a simple, one pass statistical approach as underpinning for the sampling schedule. Using a Bayesian statistical approach introduces the concept of Prior information giving an adaptive sample strategy based on previous knowledge. This aligns more closely with the iterative approach demanded of the most commonly used structured decision-making tool in this area (Data Quality Objectives) and the potential to provide a more fully underpinned justification than the more traditional statistical approach. The approach described has been developed in a UK regulatory context but is translated to a waste stream from the Fukushima Daiichi Nuclear Power Station to demonstrate how the methodology can be applied in this context to support decision making regarding the ultimate disposal option for radioactive waste in a more global context.

• 응용통계연구
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• 제11권1호
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• pp.141-150
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• 1998

본 논문에서는 매우 민감한 조사에서 모집단이 양적속성을 갖는 여러 개의 집락으로 구성되어 있을 때, 집락을 추출단위로 하는 단순집락추출법에 양적속성의 무관질문모형을 적용하였다. 그리고, 일정한 비용하에서 분산을 최소로 하는 집락의 크기와 표본집락의 수의 최적값을 구하여 최소분산의 형태를 도출하였다. 또한, 제안한 단순집략추출법에 의한 무관질문모형과 단순임의 추출법에 의한 무관질문모형과의 효율성을 비교해 보았다.

• 응용통계연구
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• 제7권1호
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• pp.141-147
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• 1994

범죄의 성향이나 도박, 마약 복용 실태 등과 같은 사회적으로나 개인적으로 매우 민감한 문제에 대한 조사에서 세대별 또는 계층별로 상당히 차이가 나는 경우에 단순임의 추출법에 의한 Warner의 확률화 응답 기법보다 효율적인 층화 임의 추출법에 의한 층화 확률화 응답 기법을 제시하고 그 효율성을 증명하였다.

• 제어로봇시스템학회논문지
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• 제9권8호
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• pp.632-639
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• 2003

This paper proposes a simple compensation scheme for the time delay caused by measurement, calculation and selection of voltage vector in Direct Torque Control (DTC) of an induction motor. In general scheme, it is difficult to know the exact delay time, furthermore the delay time can be varied by program routines for calculation and processing of measured data. In this proposed scheme, by applying voltage vector at the beginning of next sampling period, a fixed delay time is achieved and its compensation becomes much simpler. Furthermore, with the simple compensation algorithm, an improved performance can be achieved by shortening sampling period. Experimental results prove the feasibility of the proposed scheme in induction motor control.

• 한국안전학회지
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• 제36권2호
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• pp.32-38
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• 2021

This paper suggests an approach to evaluate the reliability of an intelligent power module with information deficiency of prior distribution and the characteristics of censored data through Bayesian statistics. This approach used a prior distribution of Bayesian statistics using the lifetime information provided by the manufacturer and compared and evaluated diffuse prior (vague prior) distributions. To overcome the computational complexity of Bayesian posterior distribution, it was computed with Gibbs sampling in the Monte Carlo simulation method. As a result, the standard deviation of the prior distribution developed using simple information was smaller than that of the posterior distribution calculated with the diffuse prior. In addition, it showed excellent error characteristics on RMSE compared with the Kaplan-Meier method.

• International Journal of Contents
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• 제3권1호
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• pp.1-8
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• 2007

The distributions of the minimum correlated F-variable arises in many applied statistical problems including simultaneous analysis of variance (SANOVA), equality of variance, selection and ranking populations, and reliability analysis. In this paper, negative binomial sampling technique is employed to derive the distributions of the minimum of chi-square variables and hence the distributions of the minimum correlated F-variables. The work presented in this paper is divided in two parts. The first part is devoted to develop some combinatorial identities arised from the negative binomial sampling. These identities are constructed and justified to serve important purpose, when we deal with these distributions or their characteristics. Other important results including cumulants and moments of these distributions are also given in somewhat simple forms. Second, the distributions of minimum, chisquare variable and hence the distribution of the minimum correlated F-variables are then derived within the negative binomial sampling framework. Although, multinomial theory applied to order statistics and standard transformation techniques can be used to derive these distributions, the negative binomial sampling approach provides more information regarding the nature of the relationship between the sampling vehicle and the probability distributions of these functions of chi-square variables. We also provide an algorithm to compute the percentage points of the distributions. The computation methods we adopted are exact and no interpolations are involved.