• 응용통계연구
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• 제13권2호
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• pp.207-224
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• 2000

모집단을 집략화하여 층화 2-단 표본 추출을 할 때에 일반적으로 집락의 크기는 정해져 있다. 그러나 집락이 아파트 단지 등과 같은 경우에 집락의 크기는 큰 차이를 보인다. 이 경우 집락을 합치거나 또는 분할할 필요가 생긴다. 대 표본조사(large sample survey)에서 행정상 또는 조사 편의상 동질의 원소들이 집락화 되어 있고 집락의 크기를 결정할 필요가 있을 경우가 고려되었으며 본 논문에서는 집락의 최적크기를 결정하는 문제를 다루었다. 또한 주어진 비용 하에서 최적의 일차 추출 단위 수와 최적의 이차 추출 단위 수를 구하였다.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.57-69
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• 2007

Adaptive cluster sampling design is known as a sampling method for rare clustered population. Three modified adaptive cluster sampling designs are proposed. The adjusted Hansen-Hurwitz estimator and the Horvitz-Thompson estimator are considered. Efficiency issue of the proposed sampling designs is discussed in a Monte-Carlo simulation study.

• Journal of the Korean Statistical Society
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• 제27권3호
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• pp.265-278
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• 1998

In this paper, we suggest two-phase adaptive cluster sampling schemes. The main feature of the two-phase sampling is that the information collected in the first phase sample is utilized in the selection of the second phase sample. The conventional two-phase sampling is, however, not sufficient to increase efficiency when the population of interest is rare and clustered. In the proposed sampling scheme, the first phase sample is selected with adaptive cluster sampling procedure and the second phase sample is selected by PPSWR and $\pi$PS sampling. We investigate unbiased estimators of population total and their variance for the proposed sampling schemes respectively. Finally we compare these suggested sampling schemes using numerical examples .

• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.459-469
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• 1998

Balanced half sample method is a simple variance estimation method for complex sampling designs. Since it is simple and flexible, it has been widely used in large scale sample surveys. However, the usual BHS method overestimate the true variance in without replacement sampling and two-stage cluster sampling. Focusing on this point , we proposed an unbiased BHS variance estimator in a stratified two-stage cluster sampling and then described an implementation method of the proposed estimator. Finally, partially BHS design is explained as a tool of reducing the number of replications of the proposed estimator.

• 응용통계연구
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• 제25권3호
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• pp.447-456
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• 2012

For a sensitive survey in which the population is comprised of several clusters with a quantitative attribute, we present an additive quantitative randomized response model by cluster sampling that adapts a two-stage cluster sampling instead of a simple random sample based on Himmelfarb-Edgell's additive quantitative attribute model and Gjestvang-Singh's one. We also derive optimum values for the number of 1st stage clusters and the optimum values of observation units in a 2nd stage cluster under the condition of minimizing the variance given constant cost. We can see that Himmelfarb-Edgell's model is more efficient than Gjestvang-Singh's model under the condition of cluster sampling.

• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.939-950
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• 2003

Control charts are very useful tool for monitoring of process characteristics. This paper discusses the problem of design of control limits when the subgroups are composed by cluster sampling type. As an alternative method of design of control limits XbBar chart is proposed, which uses the control limits based on the variation between subgroups instead of using classical variation within subgroups. Two examples are presented for reasonable design of control limits and conditions of subgroups based on the cluster sampling. Through examples the guidelines for making proper control limits are proposed.

• 한국산림과학회지
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• 제95권3호
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• pp.370-377
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• 2006

본 연구는 지속가능한 산림경영에 적합한 표본조사 방법을 제시하기 위해 계통적 추출법, 계통적 집락추출법, 그리고 층화집락추출법을 이용하여 경기도 양평군의 산림을 대상으로 자료를 수집한 후 통계검증을 실시하였다. 표본조사 방법은 계통적 집락추출법이 가장 효율적인 것으로 분석되었는데, 계통적 집락추출법을 적용할 경우 집락의 형태와 집락 내의 표본점 간의 거리를 결정하기 위해 5가지 집락의 형태와 표본점간의 거리 4가지에 대하여 통계검증을 실시하였다. 그 결과 집락의 형태는 삼각형 그리고 집락 내의 표본점 간의 거리는 50m가 가장 적합할 것으로 평가되었다.

• Journal of the Korean Statistical Society
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• 제34권1호
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• pp.73-83
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• 2005

Thompson's (1990) adaptive cluster sampling is a promising sampling technique to ensure effective representation of rare or localized population units in the sample. We consider the problem of simultaneous estimation of the numbers of earners through a number of rural unorganized industries of which some are concentrated in specific geographic locations and demonstrate how the performance of a conventional Rao-Hartley-Cochran (RHC, 1962) estimator can be improved upon by using auxiliary information in the form of generalized regression (greg) estimators and then how further improvements are also possible to achieve by adopting adaptive cluster sampling.

• Communications for Statistical Applications and Methods
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• 제6권1호
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• pp.107-116
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• 1999

We propose a sample design which minimize Bayes risk for cluster smpling in sampling inspection. We treat a pilot sample and an additional sample size as random variable. In addition we compute an appropriate cluster size for handling over-dispersion.

• Communications for Statistical Applications and Methods
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• 제23권1호
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• pp.47-55
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• 2016

Stratified cluster sampling has been widely used for effective parameter estimations due to reductions in time and cost. The probability proportional to size (PPS) sampling method is used when the number of cluster element are significantly different. However, simple random sampling (SRS) is commonly used for simplicity if the number of cluster elements are almost the same. Also it is known that the ratio estimator produces a good performance when the total number of population elements is known. However, the two stage cluster estimator should be used if the total number of elements in population is neither known nor accurate. In this study we suggest a composite estimator by combining the ratio estimator and the two stage cluster estimator to obtain a better estimate under a certain population circumstance. Simulation studies are conducted to compare the superiority of the suggested estimator with two other estimators.