• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.155-165
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• 1995

초모집단(superpopulation)으로 부터 반복적으로 유한모집단을 추출할 때, 이미 조사된 자료들을 이용하면 현재의 유한모집단 모수들을 ㄷ더 효율적으로 추정할 수 있다. 이러한 문제에 대하여 Ericson(1969)이 유한모집단 표본추출에서 베이지안 분석을 하였고, Ghosh와 Meeden(1986)은 정규 초모집단을 가정하여 유한모집단 평균의 경험적 베이즈 추정을 하였다. Nandram과 Sedransk (1993)는 Ghosh와 Meeden(1986)의 유한모집단들의 분산이 모두 같다는 가정들을 완화하여 유한집단 평균의 경험적 베이즈 추정을 하였다. 본 연구는 Nandram과 Sedransk의 결과를 층과표본추출의 경우로 일반화 하였다.

• Survey Research
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• v.5 no.1
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• pp.93-101
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• 2004

In business survey, cut-off sampling is usual, The contribution from cut-off part of the population is at least small in comparison with the remaining population. In this case, part of the target population is excluded from the selection and parameter estimations are only based on Take-all and Take-some stratum. It may be tempting not to use resources on enterprises that contribute little to the overall results of the survey. And this reduces the response burden for these small enterprises. But, the size of cut-off stratum has been increased as a way to manage reduced budgets. This leads to additional bias. In this study, the population have been separated as three stratum, cut -off, take-some, take-all, and we will estimate cut-off part using auxiliary variable.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.513-521
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• 2014

If the target error of an estimator at the present time is greater than the coefficient of variation(CV) of the estimator at the previous time, sample size at this point should be decreased. Various papers have researched sample size determination methods using the CV of an estimator at the previous time, variation of population size and target error of the estimator at this time in sampling on successive occasions. We research a new sample size determination method additionally using change of population CV. We compare the proposed method with existing ones in various simulation settings.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.142-149
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• 2013

In general, a color of an image is represented by using red, green, and blue channels in a RGB camera system. However, only information of three channels are limited to estimate a spectral reflectance of a real scene. Because of this, the RGB camera system can not accurately represent the color. To overcome this limitation and represent an accurate color, researches to estimate the spectral reflectance by using a multi-channel camera system are being actively proceeded. Recently, a reflectance estimation method adaptively constructing a similar training set from a traditional training set according to a camera response by using a spectral similarity was introduced. However, in this method, an accuracy of the similar training set is reduced because the spectral similarity based on an average and a maximum distances was applied. In this paper, a reflectance estimation method applied a spectral similarity based on a correlation coefficient is proposed to improve the accuracy of the similar training set. Firstly, the correlation coefficient between the similar training set and the spectral reflectance obtained by Wiener estimation method is calculated. Secondly, the similar training set is constructed from the traditional training set according to the correlation coefficient. Finally, Wiener estimation method applied the similar training set is performed to estimate the spectral reflectance. To evaluate a performance of the proposed method with previous methods, experimental results are compared. As a result, the proposed method showed the best performance.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.11a
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• pp.77-85
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• 2001

In this paper we study the calibration estimator and its variance estimator for the population total using a bootstrap method according to the levels of an auxiliary information having strong correlation with an interested variable in nonresponse situation. At this point, we find tire calibration estimator in case of auxiliary information for population and sample, and then we drive the bootstrap variance estimator of it. By simulation study we compare the efficiencies with the Taylor and Jackknife variance estimators.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1115-1128
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• 2011

Cut-off sampling that discards a part of the population from the sampling frame, is a widely used method for a highly skewed population like a business survey. Usually to the estimate of population total, we need to estimate the total of the take-nothing stratum. Many estimators have been developed to estimate the total of the take-nothing stratum. In this paper, we suggest a new composite estimator which combines the estimator suggested by Sarndal et al. (1992) and a ratio estimator obtained by small samples from the take-nothing stratum. Small simulation studies are performed for the comparison of the estimators and we confirm that the new suggested estimator is superior to the others.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.131-136
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• 2003

소지역에서 유한모집단의 총계등을 추정하는데 있어서 모형-기반 합성치를 예측한다. 즉, 예측(prediction) 문제로 추정치를 다룬다. 초모집단(super-population) 확률 모형을 세우고 최적의 예측치를 유도한다.

• Proceedings of the Korean Association for Survey Research Conference
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• 2003.06a
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• pp.29-33
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• 2003

In business survey. cut off sampling is usual. The contribution from cut off part of the population is at least small in comparison with the remaining population. In this study. the population have been separated as three stratum, cut-off. take-some, take-all, and we will estimate cut-off part.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.117-122
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• 2003

개방형 모집단에 대한 Mark-Recapture방법은 일반적으로 Jolly-Seber방법이 사용된다. 이 방법은 각 표본에서 모든 동물들에 대한 포획될 확률은 동일하다는 가정을 갖는다. 개방형 모집단에 대한 Mark-Recapture방법을 소개하고 폐쇄형 모집단에서 갖는 문제를 해결하기 위한 개방형 모집단의 로버스트 설계를 이용하려다. Pollock과 Kendall의 Jolly-Seber 모수로부터 유도된 모집단의 성장율 추정에 대하여 SAS와 POPAN의 결과를 비교하였다.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.271-276
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• 2004

이 논문에서는 유한모집단 분포함수에 대한 추정량들을 소개하고, 이론적인 측면과 경험적인 측면으로 비교하였다 분포함수 추정량은 설계기반 특성을 갖는 추정량과 모형기반 특성을 갖는 추정량으로 구분되며, 각각 설계기반 특성과 모형기반 특성을 갖는다. 수치적인 비교를 위하여 분포함수 추정량들을 2000년 인구주택 총 조사의 서울 가구수와 가구원수 데이터에 적합하여 비교하였다.