• Journal of Korean Society of Forest Science
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• v.46 no.1
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• pp.29-43
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• 1980

The sampling methods selected for this area was (1) Simple random sampling (2) Systematic sampling and (3) Sub-sampling. For the calculation of the number of sampling plot, 10 % coefficient of variation was adapted. As a result, 57 plots each for simple random sampling and systematic sampling was calculated. In the sub-sampling method, however, total of 40 plots, which were consisted of 5 Blocks, secondary 4 major units and tertiary 2 minor units, were examined. The reuslts obtained are summarized as follows : 1. The rate of expected error was 9.24% for simple random sampling, 8.36% for systematic sampling and 7.54% for sub-sampling, respectively. Therefore, the sub-sampling was proved to be the most accurate method among the test. 2. The volume calculated by each sampling method was compared to the volume of all stand. The rate of expected error was also lowest in the sub-sampling (0.39%), followed by systematic sampling (4.18%) and simple random sampling (7.92%). 3. Comparing the various reuslts and analysis of these results, the sub-sampling was regarded as the most rapid and economical method because this method had not only the least number of plots but also the least expected error among the tested sampling methods Therefore the sub-sampling is proved to be an ideal sampling method for forest survey.

• Journal of Korean Society of Forest Science
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• v.95 no.3
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• pp.370-377
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• 2006

This study was conducted to develop an efficient method of sampling design appropriate for the sustainable forest management. For this, data were collected in Yangpyung-Gun, Gyunggi Province based on three different sampling designs such as systematic design, systematic cluster design, and stratified cluster design. Based on evaluation statistics, the sampling designs were compared to select a sampling method fitted to sustainable forest management. It was found that the systematical cluster sampling is the most efficient sampling method in terms of feasibility for sustainable forest management. It was also recommended that the sample plots should be made as a cluster of triangle-shape. The clusters should be consisted of a main plot and three sub-plots. And the sub-plots should be arranged with a distance of 50m from the main plot in the center of cluster.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.379-391
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• 2001

본 연구에서는 관심거리가 되고 있는 마코프인쇄 몬테칼로(Markov Chain Monte Carlo, MCMC)방법에 근거한 공간 확률난수 (spatial random variate)생성법과 깁스표본추출법(Gibbs sampling)에 의한 베이지안 분석 방법에 대한 기술적 사항들에 관하여 검토하였다. 먼저 기본적인 확률난수 생성법과 관련된 사항을 살펴보고, 다음으로 조건부명시법(conditional specification)을 이용한 공간 확률난수 생성법을 예를 들어 살펴보기로한다. 다음으로는 이렇게 생성된 공간자료를 분석하기 위하여 깁스표본추출법을 이용한 베이지안 사후분포를 구하는 방법을 살펴보았다.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.393-400
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• 2011

In a large scale survey, cluster sampling design in which a set of observation units called clusters are selected is often used to satisfy practical restrictions on time and cost. Especially, a two stage cluster sampling design is preferred when a strong intra-class correlation exists among observation units. The sample Primary Sampling Unit(PSU) and Secondary Sampling Unit(SSU) size for a two stage cluster sample is determined by the survey cost and precision of the estimator calculated. For this study, we derive the optimal sample PSU and SSU size when the population SSU size across the PSU are di erent by extending the result obtained under the assumption that all PSU have the same number of SSU. The results on the sample size are then applied to the $4^{th}$ Korea Hospital Discharge results and is compared to the conventional method. We also propose the optimal sample SSU (discharged patients) size for the $7^{th}$ Korea Hospital Discharge Survey.

• Computational Structural Engineering
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• v.4 no.2
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• pp.119-129
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• 1991

This study is directed for the development of an efficient Importance Sampling Technique for system reliability analysis of bridge structures. Many methods have been proposed for structural reliability assessment such as the First-order Second-Moment Method, the Advanced Second-Moment Method, Monte Carlo Simulation, etc. The Importance Sampling Technique can be employed to obtain accurate estimates for the system reliability with reasonable computation effort. Based on the results of example analysis, it may be concluded that Importance Sampling Technique is a very effective tool for the system reliability analysis.

• Proceedings of the Korean Association for Survey Research Conference
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• 2000.11a
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• pp.157-168
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• 2000

In this paper we point out another advantage of systematic sampling over simple random sampling, which have not yet been spelled out in the literature. After a single sample is drawn by a sampling scheme, it is important to check whether the achived sample represents the population well or not. Therefore, a sampling scheme which avoids the possibility of selecting non-preferred samples is desirable. The simulation results are given to illustrate that, in the ordered population, the possibility of selecting non-preferred sample by systematic sampling is lower than that by simple random sampling.

• Proceedings of the KSCN Conference
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• 2003.05a
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• pp.98-99
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• 2003

본 연구의 목적은 한국인과 일본인, 중국인의 선호 음식류 및 조리법과 외식행동을 조사하여 향후 일본, 중국 외식시장 진출 시 기초 도구를 마련하고자 하였다. 연구 대상은 인천국제공항 면세구역 내 출국을 대기하는 한국인과 일본인, 중국인을 대상으로 하였다. 설문 표본 추출은 연구자의 판단에 의한 판단 표본추출법을 사용하였다. 설문방법은 연구자 외 1인이 직접 설문하였다. (중략)

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.59-62
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• 2003

양곡소비량조사는 가구부문과 사업체부문에서 소비하는 주요식품의 소비량을 조사함과 동시에 주곡인 쌀의 재고량을 조사한다. 이 조사는 해마다 통계청에서 표본설계하여 실시하는 표본조사에 의존한다. 특히, 사업체 부문의 양곡소비량 조사는 응용절사법(Modified Cut-Off Sampling Method)에 의해 표본추출되어 매년 11월에 조사가 실시되고 있다. 2003년도 사업체 양곡소비량 및 재고량조사는 2001년 사업체기초통계조사와 광공업통계조사를 모집단으로 하여 22,320 대상업체 중에서 819업체를 전수조사하고 2,145 업체를 표본조사하여 전체를 추계한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.1
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• pp.93-104
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• 1982

DTM data evaluation regards accuracy and efficiency as its most important parameters, and these two elements are sensitively influenced by the sampling technique employed and its procedure. This study attempts to improve sampling techniques and evaluate the accuracy of DTM based on earth volume calculation, using aerial photography and field survey information as basic DTM data, and a regular grid and progressive sampling method for sampling process. Especially, the progressive sampling has employed different combinations of threshold and sampling criteria, that is, parameters influential on efficiency and accuracy, for the purpose of numerical tests intended to permit comparative analysis. The tests have resulted in the findings that in progressive sampling its grid density fits in well with given terrain characteristics in proportion to their variability. and that threshold and accuracy contain close mutual relations in which an increased threshold bringing more efficency but less accuracy. It follows, therefore. that a threshold, when appropriately determined, can be of very efficient use for considerably broad applications, and particularly, will play a significant role in its application to such general civil engineering as earth volume calculation.

• Survey Research
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• v.11 no.1
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• pp.123-144
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• 2010

In new sample design for Korea Housing Survey to research about housing policy, total strata are forty five because individual results of sixteen regions are estimated. The sample size is determined by sample errors of several variables which are the living area, family income, householder income, and living expenses. The sample size of each region is determined by relative standard error of existing result, and the strata sample size is to use the square root proportion allocation. Enumeration districts are sampled by the probability proportion to size systematic sampling in proportion to the enumeration district size, and the systemic sampling to use assortment characteristics. We considered a new apartment complex because of variation reflections which are rebuilder and redevelopment of houses. To get estimators of mean and variance, we used the design weighting, non-response adjusting, and post-stratification. In order to consider estimation efficiency, we calculate the design effect using estimators of variance.