• Title/Summary/Keyword: 잭나이프 분산추정

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잭나이프 방법을 이용한 비추정

  • 조길호;조장식;김상룡;이우동
    • Communications for Statistical Applications and Methods
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    • v.4 no.1
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    • pp.301-310
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    • 1997
  • 본 연구에서는 비(ratio)에 대한 2차잭나이프 추정량을 제안하고, 그것의 편의와 분산이 집단의 수에 대한 감소함수임을 보인다. 또한, 이 추정량의 우수성을 편의와 평균제곱오차의 측면에서 기존의 추정량과 비교 분석한다.

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시군구 실업자 추정에서 분산 추정

  • Lee, Gye-O;Kim, Gyu-Yeong
    • Proceedings of the Korean Statistical Society Conference
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    • 2002.05a
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    • pp.7-12
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    • 2002
  • 경제활동인구조사에서 시군구의 실업자를 추정하는데 소지역 추정법을 이용하는 방안에 대한 연구는 관심의 대상이 되고 있다. 본 연구에서는 합성 추정법과 복합 추정법을 이용한 시군구 실업통계 작성법을 소개하였고 추정량이 편향이므로 잭나이프 방법을 이용한 추정량의 정도를 계산하는 절차를 설명하였으며, 광주광역시의 구별 실업통계작성을 사례로 제시하였다.

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Estimation of the Number of the Unemployed Using Small Area Estimation Methods (소지역 추정방법을 이용한 실업자 수 추정 사례연구)

  • Kwon, Se-Hyug
    • Survey Research
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    • v.10 no.1
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    • pp.141-154
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    • 2009
  • With the current sampling scheme, the sampling variance is getting larger in producing smaller regional statistics than the designed area, The larger sample size can make the variance reduced but the efficiency of sample survey lower. The desired confidence level of sampling survey can be obtained using the current sample scheme with the same sample size and administrative data. In this paper, the number of the unemployed of 5 regions in Daejon are estimated using small area estimation methods and the CV values in each estimation method is calculated and compared for their estimation efficiency as empirical study. Jackknife method is proposed to estimate the MSE of synthetic estimator and composite estimator more accurately.

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Bootstrap Variance Estimation for Calibration Estimators in Stratified Sampling (층화 추출에서 보정추정량에 대한 붓스트랩 분산 추정)

  • 염준근;정영미
    • Proceedings of the Korean Association for Survey Research Conference
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    • 2001.11a
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    • pp.77-85
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    • 2001
  • In this paper we study the calibration estimator and its variance estimator for the population total using a bootstrap method according to the levels of an auxiliary information having strong correlation with an interested variable in nonresponse situation. At this point, we find tire calibration estimator in case of auxiliary information for population and sample, and then we drive the bootstrap variance estimator of it. By simulation study we compare the efficiencies with the Taylor and Jackknife variance estimators.

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Weighted Hot-Deck Imputation in Farm and Fishery Household Economy Surveys (농어가경제조사에서 가중핫덱 무응답 대체법의 활용)

  • Kim Kyu-Seong;Lee Kee-Jae;Kim Jin
    • The Korean Journal of Applied Statistics
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    • v.18 no.2
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    • pp.311-328
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    • 2005
  • This paper deals with a treatment of nonresponse in farm and fishery household economy surveys in Korea. Since the samples in two surveys were selected by stratified multi-stage sampling and weighted sample means has been used to estimate the population means, we choose a weighted hot-deck imputation method as an appropriate method for two surveys. We investigate the procedure of the weighted hot-deck as well as an adjusted jackknife method for variance estimation. Through an empirical study we found that the method worked very well in both mean and variance estimation in two surveys. In addition, we presented a procedure of forming imputation class and formed four imputation classes for each survey and then compared them with analysis. As a result, we presented two most efficient imputation classes for two surveys.

A sample survey design for service satisfaction evaluation of regional education offices (지역교육청 수요자 만족도조사를 위한 표본설계에 관한 연구)

  • Heo, Sun-Yeong;Chang, Duk-Joon
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.4
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    • pp.669-679
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    • 2010
  • A sample survey design is suggested for the service satisfaction evaluation of regional education offices based on the sample size of 2009 Gyeongnam regional education offices's customer satisfaction survey. The sample design is developed to fit the goal of evaluation of individual regional offices and allocate at least the minimum sample size to each city or county in Gyeongnam to achieve the goal of the survey. The population is stratified according to the regions and the types of schools, and the sample of schools is selected with proportional to the size of classes within each stratum. Finally, each sample student is selected according to two-stage cluster sampling within each sample school. Weighting averages, weighting totals and so on can be evaluated for analysis purposes. Their variance estimates can be evaluated using re-sampling methods like BBR, Jackknife, linearization-substitution methods, which are generally used for the data from a complex sample.