• Communications for Statistical Applications and Methods
• /
• v.22 no.5
• /
• pp.435-443
• /
• 2015

Two phase sampling design is useful to increase estimation efficiency using deep stratification, improved non-response adjustment and reduced coverage bias. The same sampling units are commonly used for the first and the second phases in complex two-phase sampling design. In this paper we consider a sampling scheme where the first phase sampling units are clusters and the second phase sampling units are list samples. Using selected clusters in first phase requires that we list up elements in the selected clusters from the first phase and then use the list as a secondary sampling frame for the second phase sampling design. Then we select second phase samples from the listed sampling frame. We suggest an estimator based on the complex two-phase sampling design with different sampling units of each phase. Also the estimated variances of the estimator obtained by using classic and replication variance methods are considered and compared using simulation studies. For real data analysis, 2010 Korea Farm Household Economy Survey (KFHES) and 2011 Korea Agriculture Survey (KAS) are used.

• Survey Research
• /
• v.10 no.1
• /
• pp.155-168
• /
• 2009

As an efficient sampling design, stratified random sampling is often used when auxiliary information is available at the designing stage. Although one - per - stratum design is an efficient design that can be used when many auxiliary variables are available, it does not provide any unbiased variance estimator. With a two - per - stratum sample in which two elements are selected from each stratum, it is possible to obtain an unbiased variance estimator. However the loss of efficiency could be significant if any important stratification variable is missed. In this study, we investigated a sampling design that uses the all given auxiliary information and also permits an unbiased variance estimator suggested by Park and Fuller(2008). Through a simulation study, we compared several stratified random sampling and systematic sampling design. We also applied the proposed stratified sampling designs to 2007 youth panel data.

• Journal of Korean Society for Quality Management
• /
• v.43 no.4
• /
• pp.471-488
• /
• 2015

Purpose: Double sampling $T^2$ chart is a useful tool for detecting a relatively small shift in process mean when the process is controlled by multiple variables. This paper finds the optimal design of the double sampling $T^2$ chart in both economical and statistical sense under Weibull failure model. Methods: The expected cost function is mathematically derived using recursive equation approach. The optimal designs are found using a genetic algorithm for numerical examples and compared to those of single sampling $T^2$ chart. Sensitivity analysis is performed to see the parameter effects. Results: The proposed design outperforms the optimal design of the single sampling $T^2$ chart in terms of the expected cost per unit time and Type-I error rate for all the numerical examples considered. Conclusion: Double sampling $T^2$ chart can be designed to satisfy both economic and statistical requirements under Weibull failure model and the resulting design is better than the single sampling counterpart.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.28 no.11
• /
• pp.1760-1767
• /
• 2004

Sequential sampling approaches of a metamodel that sampling points are updated sequentially become a significant consideration in metamodeling technique. Sequential sampling design is more effective than classical space filling design of all-at-once sampling because sequential sampling design is to add new sampling points by means of distance between sampling points or precdiction error obtained from metamodel. However, though the extremum points can strongly reflect the behaviors of responses, the existing sequential sampling designs are inefficient to approximate extremum points of original model. In this research, new sequential sampling approach using the sensitivity of Kriging model is proposed, so that new approach reflects the behaviors of response sequentially. Various sequential sampling designs are reviewed and the performances of the proposed approach are compared with those of existing sequential sampling approaches by using mean squared error. The accuracy of the proposed approach is investigated against optimization results of test problems so that superiority of the sensitivity approach is verified.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.4
• /
• pp.939-950
• /
• 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.

• Survey Research
• /
• v.6 no.2
• /
• pp.1-32
• /
• 2005

The accuracy of exit poll mainly depends on the sampling method of voting places. For exit poll, we propose a probability sampling method of selecting voting places as an alternative to the bellwether polling place sampling. Through an empirical study based on the 2004 general election data, the efficiency of the suggested systematic sampling from ordered voting places was evaluated in terms of mean prediction error and it turns out that the proposed sampling method outperformed the bellwether polling places sampling. We also calculated the variance of estimator from the proposed sampling, and considered the sample size problem to guarantee the target precision using the design effect of the proposed sample design.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.1726-1729
• /
• 1997

In general, the solvability of linear robust output regulation problem are not preserved under time-sampling. Thus, it is found that the digital regulator implemented by itme-sampling of anlog output regulator designed based on the continuous-time linear system model is nothing but a 1st order approximation with respect to time-sampling. By the way, one can design an improved sampled-data regulator with respect to sampling time by utilizing the intrinsic structure of the system. In this paper, we study the system structures which it is possible to design an improved sampled-data regulator with respect to sampling time.

• Communications for Statistical Applications and Methods
• /
• v.14 no.1
• /
• pp.57-69
• /
• 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 Korean Institute of Industrial Engineers
• /
• v.25 no.1
• /
• pp.8-20
• /
• 1999

In this paper, a design and estimation procedure for the stochastically dependent nonstationary multiple acceptance sampling plans is developed. At first, the rough-cut acceptance and rejection numbers are given as an initial solution from the corresponding sequential sampling plan. A Monte-Carlo algorithm is used to find the acceptance and rejection probabilities of a lot. The conditional probability formula for a sample path is found. The acceptance and rejection probabilities are found when a decision boundary is given. Several decision criteria and the design procedure to select optimal plans are suggested. The formula for measuring performance of these sampling plans is developed. Type I and II error probabilities are also estimated. As a special case, by setting the stage size as 1 in a dependent sampling plan, a sequential sampling plan satisfying type I and II error probabilities is more accurate and a smaller average sample number can be found. In a numerical example, a Polya dependent process is examined. The sampling performances are shown to compare the selection scheme and the effect of the change of the dependency factor.

• Journal of the Korea Safety Management & Science
• /
• v.10 no.2
• /
• pp.205-210
• /
• 2008

The research interprets the principles of sampling error design for quality statistics models such as hypothesis test, interval estimation, control charts and acceptance sampling. Introducing the proper discussions of the design of significance level according to the use of hypothesis test, then it presents two methods to interpret significance by Neyman-Pearson and Fisher. Second point of the study proposes the design of confidence level for interval estimation by Bayesian confidence set, frequentist confidential set and fiducial interval. Third, the content also indicates the design of type I error and type II error considering both productivity and customer claim for control chart. Finally, the study reflects the design of producer's risk with operating charistictics curve, screening and switch rules for the purpose of purchasing and subcontraction.