• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.25-37
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• 1996

In this paper, we propose regression estimators based on a partial replacement sampling scheme over two successive occasions and derive the minimum variances of them. PPSWR, RHC, $\pi$PS and PPSWOR schemes are considered to select unequal probability samples on two occasions. Simulation results over four populations are given for comparison of composite estimators and regression estimators.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.145-162
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• 1993

We review sampling schemes on successive occasions with partial replacement of units and propose a Rao-Hartley-Cochran(RHC) type's sampling scheme over two successive occasions with probability proportionate to observations on the previous occasion. For comparison of the reviewed and proposed sampling schemes, optimal estimator of population mean on second occasion and its variance are derived. The relative efficiency of the proposed sampling scheme is compared with other equal and unequal probability sampling scheme by theoretical and numerical simulation study. For simulation study, three artificial populations are generated by a time series model. It is observed that RHC type's sampling scheme has small variance and deviation in general.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.69-80
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• 2006

In this study, we make an effort to find a method to acquire sensitive information when sensitive populations are consisted of several clusters that vary in size. We suggest and systemize the theoretical validity for applying RRT(Randomized Response Technique) to PPS(Probability Proportional to Size) sampling method and derive the estimate and it's variance of the proportion of sensitive characteristic of population by using the suggested method. We compare the efficiency of the suggested technique by two-stage equal probability sampling. We examine practical aspects of the suggested method of RRT by PPS sampling through field survey.

• 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 the Korean Data and Information Science Society
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• v.28 no.2
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• pp.443-449
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• 2017

Estimation of population proportion like the distribution rate of LED TV and the prevalence of a disease are often estimated based on survey sample data. Population proportion is generally considered as a special form of population mean. In complex sampling like stratified multistage sampling with unequal probability sampling, the denominator of mean may be random variable and it is estimated like ratio estimator. In this research, we examined the estimation of distribution rate based on stratified multistage sampling, and determined some numerical outcomes using stratified random sample data with about 25% of missing observations. In the data used for this research, the survey weight was determined by deterministic way. So, the weights are not random variable, and the population distribution rate and its variance estimator can be estimated like population mean estimation. When the weights are not random variable, if one estimates the variance of proportion estimator using ratio method, then the variances may be inflated. Therefore, in estimating variance for population proportion, we need to examine the structure of data and survey design before making any decision for estimation methods.

• Journal of the Korean Statistical Society
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• v.16 no.2
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• pp.92-101
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• 1987

Our interest is to access in some way teh relative odds or probability that a multivariate observation Z belongs to one of k multivariate normal populations with unequal covariance matrices. We derived the empirical Bayes posterior odds ratio for the classification rule when population parameters are unknown. It is a generalization of the posterior odds ratio suggested by Gelsser (1964). The classification rule does not have complicated distribution theory which a large variety of techniques from the sampling viewpoint have. The proposed posterior odds ratio is compared to the Gelsser's posterior odds ratio through a Monte Carlo study. The results show that the empiricla Bayes posterior odds ratio, in general, performs better than the Gelsser's. Especially, for large dimension of Z and small training sample, the performance is prominent.

• Journal of the Korean Statistical Society
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• v.34 no.4
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• pp.297-309
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• 2005

We consider 'efficiency versus privacy-protection' problem concerned with several well-known randomized response (RR) devices to estimate pro­portion of people bearing a stigmatizing characteristic in a community. The literature of RR on respondent's privacy protection discusses only about response specific jeopardy measures. We propose a measure of jeopardy that is independent of the RR offered by the interviewee and recommend it for using as a technical characteristic of the RR device. For ensuring better cooperation from the interviewees this new measure that depends only on the design parameters of the RR devices may be disclosed to the respondents before producing the RR by implementing the randomization device.