• Title/Summary/Keyword: 확률비례추출법

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A Study on the Randomized Response Technique by PPS Sampling (확률비례추출법에 의한 확률화응답기법에 관한 연구)

  • Lee Gi-Sung
    • 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.

A Comparison of PPS and Simple Cluster Sampling in Large Scale Sampling -Based on Economically Active Population Survey Sample Design (대규모 표본설계에서 확률비례 및 단순집락추출법 비교 -경제활동인구 표본조사 사례를 중심으로-)

  • 윤연옥;이상은
    • The Korean Journal of Applied Statistics
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    • v.14 no.1
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    • pp.1-11
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    • 2001
  • In PPS sampling, measure of size(MOS) is used to determine the probability of selection of sampling unit. However, some large scale surveys conducted in NSO(National Statistical Office) showed that the sampling units have the similar MOS. In such case, simple cluster sampling method instead of PPS sampling is recommended to give the interviewers a similar work load. In this paper, MSE and CV of the above two sampling methods applied to the 1997 Economically Active Population Survey sample design are compared.

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A Study on the Stratified Cluster Replicated Systematic Unrelated Question Model (층화 집락 반복계통 무관질문모형에 관한 연구)

  • Lee, Gi-Sung
    • The Korean Journal of Applied Statistics
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    • v.26 no.2
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    • pp.209-222
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    • 2013
  • We apply stratified cluster sampling to a replicated systematic unrelated question model for a large scale survey in which the population is comprised of several strata developed by several clusters and with sensitive parameters. We first present a replicated systematic unrelated question model using an unrelated question model to procure sensitive information from the population of clusters and then develop a suggested model to an unrelated question by a stratified cluster replicated systematic sampling that can be used in large population of strata. We cover the proportional and optimum allocation for the suggested model. Finally, we compare and analyze the efficiency of the suggested model with the replicated systematic unrelated question model.

Sample Design in Korea Housing Survey (주거 실태 및 수요조사 표본설계)

  • Byun, Jong-Seok;Choi, Jae-Hyuk
    • Survey Research
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    • v.11 no.1
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    • pp.123-144
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    • 2010
  • In new sample design for Korea Housing Survey to research about housing policy, total strata are forty five because individual results of sixteen regions are estimated. The sample size is determined by sample errors of several variables which are the living area, family income, householder income, and living expenses. The sample size of each region is determined by relative standard error of existing result, and the strata sample size is to use the square root proportion allocation. Enumeration districts are sampled by the probability proportion to size systematic sampling in proportion to the enumeration district size, and the systemic sampling to use assortment characteristics. We considered a new apartment complex because of variation reflections which are rebuilder and redevelopment of houses. To get estimators of mean and variance, we used the design weighting, non-response adjusting, and post-stratification. In order to consider estimation efficiency, we calculate the design effect using estimators of variance.

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Sample Design for Materials and Components Industry Trend Survey (부품.소재산업 동향 조사의 표본설계)

  • NamKung, Pyong
    • Communications for Statistical Applications and Methods
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    • v.15 no.6
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    • pp.883-897
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    • 2008
  • This paper provides correct informations inflecting the present situation using the sample design in population that the National Statistical Office puts in operation of the mining and manufacturing industry statistical survey in 2006. This paper proposes new sampling design which is able to grasp business fluctuations and provide basic data for the rearing policy and management of the material industry and components industry. These sample design are the modified cut-off method and multivariate Neyman allocation using principal components and sampling method is the probability proportional systematic sampling.

A Stratified Multi-proportions Randomized Response Model (층화 다지 확률화응답모형)

  • Lee, Gi-Sung;Park, Kyung-Soon
    • The Korean Journal of Applied Statistics
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    • v.28 no.6
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    • pp.1113-1120
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    • 2015
  • We propose a multi-proportions randomized response model by stratified simple random sampling for surveys of sensitive issues of a polychotomous population composed of several stratum. We also systemize a theoretical validity to apply multi-proportions randomized response model (Abul-Ela et al.' model, Eriksson's model) to stratified simple random sampling and derive the estimate and its dispersion matrix of the proportion of sensitive characteristic of population using the suggested model. Two types of sample allocations (proportional allocation and optimum allocation) are considered under the fixed cost. In efficiency, the Eriksson's model by stratified sampling are compared to the Abul-Ela et al.' model.

A Modified Horvitz-Thompson Estimator by Transformation of Variables (변수변환에 의한 수정 HORVITZ-THOMPSON 추정량)

  • 류제복
    • The Korean Journal of Applied Statistics
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    • v.17 no.1
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    • pp.27-34
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    • 2004
  • The Horvitz-Thompson(H-T) estimator is less efficient than PPS estimators in some cases. We use the two-stage variable transformation in order to remove the drawbacks and increase the efficiency of H-T estimator. We transform the auxiliary variable to use the Midzuno-Sen sampling scheme at the first stage. And the next stage, we also transform the study variable to reduce the variance of H-T estimator using the inclusion probability obtained from the first transformation. We compare the efficiency between a suggested modified H-T estimator and PPS estimators.

An Additive Stratified Quantitative Attribute Randomized Response Model (층화 가법 양적속성 확률화응답모형)

  • Lee, Gi-Sung;Ahn, Seung-Chul;Hong, Ki-Hak;Son, Chang-Kyoon
    • The Korean Journal of Applied Statistics
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    • v.27 no.2
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    • pp.239-247
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    • 2014
  • For a sensitive survey in which the population is composed by several strata with quantitative attributes, we present an additive stratified quantitative attribute randomized response model which applied stratified random sampling instead of simple random sampling to the models of Himmelfarb-Edgell's additive quantitative attribute model and Gjestvang-Singh's. We also establish theoretical grounds to estimate the stratum mean of sensitive quantitative attributes as well as the over all mean. We deal with the proportional and optimal allocation problems in each suggested model and compare the relative efficiency of the suggested two models; subsequently, Himmelfarb-Edgell's model is more efficient than Gjestvang-Singh's model under the condition of stratified random sampling.

A Stratified Mixed Multiplicative Quantitative Randomize Response Model (층화 혼합 승법 양적속성 확률화응답모형)

  • Lee, Gi-Sung;Hong, Ki-Hak;Son, Chang-Kyoon
    • Journal of the Korean Data Analysis Society
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    • v.20 no.6
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    • pp.2895-2905
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    • 2018
  • We present a mixed multiplicative quantitative randomized response model which added a unrelated quantitative attribute and forced answer to the multiplicative model suggested by Bar-Lev et al. (2004). We also try to set up theoretical grounds for estimating sensitive quantitative attribute according to circumstances whether or not the information for unrelated quantitative attribute is known. We also extend it into the stratified mixed multiplicative quantitative randomized response model for stratified population along with two allocation methods, proportional and optimum allocation. We can see that the various quantitative randomized response models such as Eichhorn-Hayre's model (1983), Bar-Lev et al.'s model (2004), Gjestvang-Singh's model (2007) and Lee's model (2016a), are one of the special occasions of the suggested model. Finally, We compare the efficiency of our suggested model with Bar-Lev et al.'s (2004) and see that the bigger the value of $C_z$, the more the efficiency of the suggested model is obtained.

A study on collecting representative food samples for the 10th Korean standard foods composition table (국가표준식품성분 데이터베이스 대표시료 선정을 위한 표본설계)

  • Kim, Jinheum;Hwang, Hae-Won;Cho, Yu Jung;Park, Jinwoo
    • The Korean Journal of Applied Statistics
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    • v.33 no.2
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    • pp.215-228
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    • 2020
  • Under Article 19, Paragraph 1 of the Food Industry Promotion Act, Rural Development Administration renews the Korean foods composition table every five years. Before the publication of the tenth revision of the Korean foods composition table in 2021, this paper suggests methods for collecting representative samples of 182 highly consumed foods in Korea. Food markets are categorized by their distribution channels, which are supermarkets and local markets. Eight samples are collected from each category by applying the National Food and Nutrient Analysis Program (NFNAP)'s stratified multi-stage sampling. The NFNAP was implemented in 1997 as a collaborative food composition research effort between the National Institute of Health (NIH) and the US Department of Agriculture (USDA) to secure reliable estimates for the nutrient content of food and beverages consumed by the US population. Selected supermarkets for selecting representative food samples are Emart Kayang, Homeplus Siheung, Lottemart Dongducheon, Emart Suwon, Lottemart Dunsan, Lottemart Yeosu, Emart Ulsan, and Hanaroclub Ulsan. Selected local markets also are Doksandongusijang in Geumcheon-gu and Pungnapsijang in Songpa-gu, Seoul, Ilsansijang in Ilsanseo-gu, Goyang, Unamsijang in Buk-gu, Gwangju, Beopdongsijang in Daedeok-gu, Daejeon, Bongnaesijang in Yeongdo-gu and Jwadongjaeraesijang in Haeundae-gu, Busan, and Jungangsijang in Jinhae-gu, Changwon.