• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.111-120
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• 2001

Either systematic sampling or stratified sampling is usually applied to the business conditions survey when companies don't have much difference in their size. But the cutoff systematic sampling is an efficient method when only a few companies are so large that the total of them almost equals to the total of whole companies. Throughout this paper, three estimators of total and their variance estimations depending on three kinds of sampling schemes are discussed, and are compared with them via their variances. It is proved that the cut-off systematic sampling is most efficient by using a real data of the logging business conditions survey.

• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.2
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• pp.19-28
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• 2018

In this paper, we studied on the sampling of ocean meteorological data to analyze signature of naval ships. The newest ocean meteorological data, that was quality controled by the Korea Meteorological Administration(KMA), was collected. Outliers were removed from the data by setting the usable range of data. After that, the data size was reduced through the random sampling method, taking geopolitical significance and effective area of buoy, for probabilistic analysis. Moreover, the sample sizes were set at 100, 200, and 400 by considering the population size and a 95% confidence level. The final sample was obtained using the two-dimensional stratified sampling method based on highly correlated water temperature and air temperature. The sum of the squared errors and the confidence interval was calculated to compare the result of sampling. As a result, this study proposed reasonable sample size for infra­red signature analysis of naval ships.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.207-224
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• 2000

Generally cluster size is predetermined when we use the stratified two-stage cluster sampling But in case that the sizes of clusters vary greatly one may want to make the sizes to be about equal. In this paper we study the optimal cluster size in stratified twostage cluster sampling. Also we find the optimal primary sampling unit sizes and optimal secondary sampling unit sizes under the given cost restriction.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.141-147
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• 1994

In the present paper an attempt has been made to develop a stratified ramdomized response technique when the respondents are selected using simple random sampling without replacement (SRSWOR) as well as simple random sampling with replacement (SRSWR). The conditions under which the proposed technique will be more efficient than the corresponding Warner's technique have been obtained.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1185-1199
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• 2016

Two phase sampling (double sampling) is often used when there is inadequate population information for proper stratification. Many recent papers have been devoted to the estimation method to improve the precision of the estimator using first phase information. In this study we suggested outlier weight adjustment methods to improve estimation precision based on the weight of the generalized ratio-cum-product estimator. Small simulation studies are conducted to compare the suggested methods and the usual method. Real data analysis is also performed.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.477-483
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• 2015

This study deals with the decision problem of sample size by the relative standard error of estimates derived from survey results in successive occasions. The population of the construction in business survey results is used to calculate quartile of the relative standard error of the 1,000 sample obtained from simple or stratified random sampling. The sample size at time t with a relative standard error of the point (t-1) in the successive occasions were calculated according to the sampling method. As a result, in terms of the sample size according to the size of the relative standard error of the (t-1), simple random sampling differs significantly from stratified sampling. In addition, we could see differences in sample size (depending on how the population is stratified) and that careful attention is required in the problem of sample size by the relative standard error of estimates derived from survey results in successive occasions.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.353-369
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• 2000

We systematized an stratified spatial sample design(SSSD) that uses the adequate stratification criteria such as the shapeness or the dispersion of an interesting region in a spatial population. And we proposed an adaptive searching estimation method in the SSSD to estimate the area of region of interest in two-dimensional surfaces. When wc adopt the proposed adaptive searching estimation method in SSSD, the observing sample size is more decreased than a classical sample design that all the designed sample size is observed. Nevertheless it has been shown that we can produce the moderate result but the efficiency is a slight reduced.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.533-543
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• 2010

Asking sensitive questions by a direct survey method causes non-response bias and response bias. Non-response bias arises from interviewees refusal to respond and response bias arises from giving incorrect responses. To rectify these biases, Warner (1965) introduced a randomized response model which is an alternative survey method for socially undesirable or incriminating behavior questions. The randomized response model is a procedure for collecting the information on sensitive characteristics without exposing the identity of the respondent. Many survey researchers have proposed diverse variants of the Warner randomized response model and applied their model to collect the information of sensitive questions. Using an optimal allocation, we proposed three-stage stratified randomized response technique which is an extension of the Kim and Elam (2005) two-stage stratified randomized response technique. In this study, we showed that the estimator based on the proposed response model is more efficient than Kim and Elam (2005). But by adding one more survey step to the Kim and Elam (2005), our proposed model may have relatively less privacy protection compared to the Kim and Elam (2005) model.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.179-184
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• 2002

층화 추출법에서 층의 경계점을 정하는 문제는 추정의 효율에 직접적으로 영향을 미치기 때문에 매우 실제적이고 중요한 문제이다. 층화변수가 일변량 연속변수인 경우 널리 알려진 방법으로는 누적도수제곱근법과 Ekman법이 있는데 이 두 방법은 모두 나름의 약점을 지니고 있다. 본 논문에서는 Breiman 등(1984)이 제시한 CART 기법 중 회귀나무(regression tree)모형을 이용하여 층의 경계점을 정하는 방법을 소개한다. 그리고 통계청의 어업총조사 자료를 사용하여 층의 경계점을 정하는 여러 다른 방법들의 효율을 비교한다.