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

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7C
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• pp.687-695
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• 2005

A bandpass sampling technique, which is a method directly downconverting a bandpass signal to a baseband or a low IF signal without analog mixers, can be an alterative choice for the SDR system to minimize the RF front-end. In this paper, a complex bandpass sampling technique for two bandpass-filtered signals is proposed. We derived generalized formulae for the available sampling range, the signal's IF and the minimum sampling frequency taking into consideration the guard-bands for the multiple RE signals. Thru the simulation experiments, the advantages of the . complex bandpass sampling over the pre-reported real bandpass sampling are investigated for applications in the SDR design.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.139-147
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• 2022

This study tried to analyze the relationship among physical activity level, subjective health status, COVID-19 Fear. This study used the 2020 Community Health Survey that includes 229,269 survey data from adults over 19 years old. The complex sampling design was applied including weight, stratification, cluster variables. Through the SPSS statistics program with complex sampling frequency analysis, complex sampling Chi-square and complex sampling regression, this study found followings. First, the group with high level of physical activity showed higher level of subjective health status than the group with low level of physical activity. Second, the group with high level of physical activity showed lower level of COVID-19 fear than the group with low level of physical activity. Third, the group with high level of subjective health status showed lower level of COVID-19 fear than the group with low level of subjective health status. However, this study has the limitation that this study did not check whether participant is diagnosed with Covid-19 or not.

• Journal of Korean Society for Quality Management
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• v.20 no.1
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• pp.158-167
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• 1992

This paper represents the new techniques for optimal sampling plans of reliability applying the mathematical complex number(real and imaginary number) in the complex system of reliability. The research formulation represent a mathematical model Which preserves all essential aspects of the main and auxiliary factors of the research objectives. It is important to formule the problem in good agreement with the objective of the research considering the main and auxilary factors which affect the system performance. This model was repeatedly tested to determine the required statistical chatacteristics which in themselves determine the actual and standard distributions. The evaluation programs and techniques are developed for establishing criteria for sampling plans of reliability effectiveness, and the evaluation of system performance was based on the complex stochastic process(derived by the Runge-Kutta method. by kolmogorv's criterion and the transform of a solution to a Sturon-Liouville equation.) The special structure of this mathematical model is exploited to develop the optimal sampling plans of reliability in the complex system.

• Journal of the Korean Statistical Society
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• v.27 no.4
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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.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1217-1225
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• 2015

Survey research, data is commonly collected through a sample design with complex design features that allow the relative efficiency on the precision of an estimator to be measured using the concept of the design effect compared to simple random sampling as a reference design. This concept is most useful when the design effect can be expressed as a function of various design features. We propose a design effect formula suitable under a stratified multistage sampling by generalizing Gabler et al. (1999, 2006)'s approaches for multistage sampling. Its use can either guide improvement in the design efficiency when in design stage or enable the evaluation of the adopted design features afterwards.

• Journal of Integrative Natural Science
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• v.5 no.4
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• pp.241-245
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• 2012

Categorical data collected based on complex sample design is not proper for the standard Pearson multinomial-based chi-squared test because the observations are not independent and identically distributed. This study investigates effects of bias of point estimator of population proportion and its variance estimator to the standard Pearson chi-squared test statistics when the sample is collected based on complex sampling scheme. This study examines the effect under two population homogeneity test. The standard Pearson test statistic can be partitioned into two parts; the first part is the weighted sum of ${\chi}^2_1$ with eigenvalues of design matrix as their weights, and the additional second part which is added due to the biases of the point estimator and its variance estimator. Our empirical analysis shows that even though the bias of point estimator is small, Pearson test statistic is very much inflated due to underestimate the variance of point estimator. In the connection of design-based variance estimator and its design matrix, the bigger the average of eigenvalues of design matrix is, the larger relative size of which the first component part to Pearson test statistic is taking.

• Journal of Integrative Natural Science
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• v.5 no.3
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• pp.157-162
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• 2012

Measurement error is one of main source of error in survey. It is generally defined as the difference between an observed value and an underlying true value. An observed value with error may be expressed as a function of the true value plus error term. In some cases, the measurement error variance may be also a function of the unknown true value. The error variance function can be rewritten as a function of true value multiplied by a scale factor. This research explore methods for estimation of the measurement error variance based on the data from complex sampling design. We consider the case in which the variance of mesurement error is a linear function of unknown true value, and the error variance scale factor is small. We applied our results to the U.S. Third National Health and Nutrition Examination Survey (the U.S. NHANES III) data for empirical analyses, which has replicate measurements for relatively small subset of initial respondents's group.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1127-1135
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• 2014

Genome-wide association studies (GWAS) are designed to discover genetic variants such as single nucleotide polymorphisms (SNPs) that are associated with human complex traits. Although there is an increasing interest in the application of GWAS methodologies to population-based cohorts, many published GWAS have adopted a case-control design, which raise an issue related to a sampling bias of both case and control samples. Because of unequal selection probabilities between cases and controls, the samples are not representative of the population that they are purported to represent. Therefore, non-random sampling in case-control study can potentially lead to inconsistent and biased estimates of SNP-trait associations. In this paper, we proposed inverse-probability of sampling weights based on disease prevalence to eliminate a case-control sampling bias in estimation and testing for association between SNPs and quantitative traits. We apply the proposed method to a data from the Korea Association Resource project and show that the standard estimators applied to the weighted data yield unbiased estimates.

• Journal of Integrative Natural Science
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• v.7 no.3
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• pp.208-213
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• 2014

National-wide and/or large scale sample surveys generally use complex sample design. Traditional Pearson chi-square test is not appropriate for the categorical complex sample data. Rao-Scott suggested an adjustment method for Pearson chi-square test, which uses the average of eigenvalues of design matrix of cell probabilities. This study is to compare the efficiency of Rao-Scott first order adjusted test to Wald test for homogeneity between two populations using 2009 Gyeongnam regional education offices's customer satisfaction survey (2009 GREOCSS) data. The 2009 GREOCSS data were collected based on stratified three-stage cluster sampling with probability proportional to size. The empirical results show that the Rao-Scott adjusted test statistic using only the variances of cell probabilities is very close to the Wald test statistic, which uses the covariance matrix of cell probabilities, under the 2009 GREOCSS data based. However it is necessary to be cautious to use the Rao-Scott first order adjusted test statistic in the place of Wald test because its efficiency is decreasing as the relative variance of eigenvalues of the design matrix of cell probabilities is increasing, specially more when the number of degrees of freedom is small.