• Survey Research
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• v.9 no.1
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• pp.69-85
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• 2008

Stratification is a feature of the majority of field sample design. This paper considers the multivariate stratification strategy for multipurpose sample survey with several auxiliary variables. In a multipurpose survey, stratification procedure is very complicated because we have to simultaneously consider the efficiencies of stratification for several variables of interest. We propose stratification strategy based on factor analysis and cluster analysis using several stratification variables. To improve the efficiency of stratification, we first select the stratification variables by factor analysis, and then apply the K-means clustering algorithm to the formation of strata. An application of the stratification strategy in the sampling design for the Fisher Production Survey is discussed, and it turns out that the variances of estimators are significantly less than those obtained by simple random sampling.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.387-394
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• 2007

This paper considers several stratification strategies for multivariate and multipurpose survey with several quantitative stratification variables. We propose three methods of stratification based on, respectively, the method of cumulative frequency square root which is the most popular one in univariate stratification, cluster analysis, and factor analysis followed by cluster analysis. We then compare the efficiency of those methods using the Dong-Eup-Myun data of the holding numbers of farming machines, extracted from the 2001 Agricultural Census. It turned out that the method based on cluster analysis with factor analysis would be a relatively satisfactory strategy.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1081-1091
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• 2010

Clinical trials are often carried out as multi-center studies because the patients enrolled for a trial study are very limited in one particular hospital. In these circumstances, the use of an ordinary Jonckheere (1954) and Terpstra (1952) test for testing trend among several independent treatment groups is invalid. We propose a the stratified Jonckheere-Terpstra test based on van Elteren (1960)'s stratified test of Wilcoxon (1945) statistics and an application of our method is demonstrated through example data. A simulation study compares the efficiency of stratified and unstratified Jonckheere-Terpstra trend tests.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2883-2893
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• 2018

We suggest a stratified conditional unrelated question randomized response model to more efficiently estimate a sensitive character A when the population is composed of several strata. In that model, only the respondents who answered "yes" through randomization device which was consisted of a less sensitive character B and a question forcing to answer "yes" respond to our suggested model and we deal with two allocation problems of proportional allocation and optimal one. We expand the suggested model into two sample stratified conditional unrelated question model to cover the case of unknowing unrelated character and deduce minimal variance through optimal sample size of stratum h. Finally, we show that the suggested model is more efficiency than stratified unrelated models and the stratified Carr et al.'s model (1982) under some given conditions, and show numerically that the smaller the values ${\pi}_{h2}$ and ${\pi}_{hy}$, the more efficiency the fit of the model.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.131-143
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• 1996

다변량 층화임의추출에서 한 변수의 Neyman 최적배분은 다른 변수에 대한 층화분산을 최소화시키지 못하는 결과를 초래할 수도 있다. 따라서 다변량 자료의 경우 '최적'배분 대신에 '절충'배분이 도입되어 왔다. 이 연구에서는 각 변수별 Neyman 최적배분에 근거해서 얻은 층화표본평균벡터의 공분산 행렬에 가장 잘 적합되는 층별로 동일한 크기의 절충배분을 찾고자 한다. 이에 적절한 기준 다섯가지를 제시하고 예를 통해 비교, 분석하였다.

• Computational Structural Engineering
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• v.11 no.4
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• pp.309-320
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• 1998

중요표본추출기법중에서도 층화표본추출법을 이용한 적응적 중요표본추출기법이 일반적으로 가장 합리적인 것으로 알려져 있다. 그러나 확률장 유한요소모형문제와 같이 기본 확률변수의 규모가 큰 경우에는 층화표본추출법에서 요구되는 기본적인 표본점의 규모가 급증하여 효율성이 떨어지게 된다. 본 연구에서는 이러한 한계성을 극복하기 위하여 층화표본추출에서 기본확률변수를 사용하는 대신에 기본확률변수들의 함수이며 새로운 확률변수인 응답값을 이용하는 방법을 개발하였다. 여기에서 응답값은 일반적인 함수형태로 표시되지 않으며, 한 번의 응답계산에 많은 계산량이 소요되므로 이러한 문제점을 해결하기 위하여 응답면식을 이용한 층화표본추출법을 개발하였다. 개발된 기법에서는 기본확률변수의 모의발생규모는 기본의 기본확률변수를 이용한 층화표본추출법에서 보다 증가하지만 매우 많은 계산량을 요구하는 실제응답해석규모는 응답면식을 이용함으로써 획기적으로 감소되었다. 특히 본 기법은 기본확률변수의 규모가 크고 대상한계상태의 파괴확률이 낮을수록 기존의 방법과 비교해 효율성이 증대되는 것으로 분석되었다.

• Tunnel and Underground Space
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• v.22 no.3
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• pp.188-195
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• 2012

Using a computational fluid dynamics (CFD) code, FLUENT, the present study investigated the thermal stratification behavior of Lyckebo storage in Sweden, which is the very first large-scale rock cavern for underground thermal energy storage. Heat transfer analysis was carried out for numerical cases with different temperatures of the surrounding rock mass in order to examine the effect of rock mass heating due to periodic storage and production of thermal energy on thermal stratification and heat loss. The change of thermal stratification with respect to time was quantitatively examined based on an index of the degree of stratification. The results of numerical simulation showed that in the early operational stage where the surrounding rock mass was less heated, the stratification of stored thermal energy was rapidly degraded over time, but the degradation and heat loss tended to reduce as the surrounding rock mass was heated during a long period of operation.

• Tunnel and Underground Space
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• v.23 no.4
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• pp.308-318
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• 2013

Thermal stratification in heat stores is essential to improve the efficiency of energy storage systems and deliver more useful energy on demand. It is generally well known that the degree of thermal stratification in heat stores varies depending on the aspect ratio (the height-to-width ratio) and size of the stores. The present study aims to investigate the effect of the aspect ratio and storage volume of rock caverns for storing hot water on thermal stratification in the caverns and heat loss to the surroundings. Heat transfer simulations using a computational fluid dynamics code, FLUENT were performed at different aspect ratios and storage volumes of rock caverns. The variation of thermal stratification with respect to time was examined using an index to quantify the degree of stratification, and the heat loss to the surroundings was evaluated. The results of the numerical simulations demonstrated that the thermal stratification in rock caverns was improved by increasing the aspect ratio, but this effect was not remarkable beyond an aspect ratio of 3-4. When the storage volume of rock caverns was large, a higher thermal stratification was maintained for a relatively longer time compared to caverns with a small storage volume, but the difference in thermal stratification between the two cases tended to decrease as the aspect ratio became larger. In addition, the numerical results showed that the heat loss to the surrounding rock tended to increase with an increase in aspect ratio because the surface area of rock caverns increased as the aspect ratio became larger. The total heat loss from multiple small caverns with a reduced storage volume per cavern was larger compared to a single cavern with the same total storage volume as that of the multiple caverns.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.133-141
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• 2010

This research suggests a unified scheme for using stratified sampling and control variates method to improve the efficiency of estimation for parameters in simulation experiments. We utilize standardized concomitant variables defined during the course of simulation runs. We first use these concomitant variables to counteract the unknown error of response by the method of control variates, then use a concomitant variable not used in the controlled response and stratify the response into appropriate strata to reduce the variation of controlled response additionally. In case that the covariance between the response and a set of control variates is known, we identify the simulation efficiency of suggested method using control variates and stratified sampling. We conjecture the simulation efficiency of this method is better than that achieved by separated application of either control variates or stratified sampling in a simulation experiments. We investigate such an efficiency gain through simulation on a selected model.

• Survey Research
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• v.11 no.3
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• pp.103-114
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• 2010

It is difficult to obtain the population information of target variables when a new sampling design for successive survey is executed. In a research of the Korea National Tourism Survey, we propose a method for selection of efficient stratification variables which are found in a combination of a existing sample data and a new frame list. At first, if there isn't common identification number between the frame list and the sample data, we find a device to substitute for absence of identification number. At second, we suggest a method to search stratification variables correlated with target variables using statistical methods like regression analysis.