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염준근,손창균 한국품질경영학회 1998 품질경영학회지 Vol.26 No.3
In sampling survey the nonresponse reduces the precision of the estimator because of the nonresponse bias of the estimator. Deville, et al.(1993) considered the generalized raking procedure with the auxiliary information under five distance measures for reducing the nonresponse bias of the estimator. This paper extends the classical weighting adjustment of Deville, et al.(1993) to the stratified sampling case with three among five measures.
새로운 量的 確率化 應答 模型 : 資料의 推定 data estimation
염준근,홍기학 東國大學校 1991 東國論叢 Vol.30 No.-
In this paper, we review and summerize the various quantitative randomized response models. We propose the new method of collecting and estimating the quantitative sensitive data by using multiproportions randomized response models, and suggest the empirical examples of that method.
異常値를 探索하는 檢定方法들의 몬테카를로 시뮬레이션에 의한 效率性 比較
廉俊根,宋民求 동국대학교 산업기술환경대학원 1996 산업기술논총 Vol.3 No.-
We have discussed a number of outlier-detection methods from the literatures. In this paper, we compare to efficiency of utlier-detection methods, that is Box-plot. KS, ESD, SW, Dixon-typed by Monte-Carlo simulation. Some of these procedures are highly susceptible to either masking or swamping. Such outlier-detection procedures are not recommended. Other procedures not recommended require specific knowledge of the number and location of the outliers. The generalized ESD procedure performs especially well under each of the conditions. It is also relatively easy to compute and interpret. We recommend this procedure highly, from the results in <Table 1> and from conclusions reached in other studies. Alternatively, a Dixon-type test in sometimes useful for very small samples.
염준근,정영미 한국조사연구학회 2001 추계학술대회 발표논문집 Vol.2001 No.-
무응답 상황하에서 보정 추정량에 대해 관심변수와 강한 상관계수를 가진 보조정보의 수준에 따라 모집단 총합에 대한 추정량과 분산추정량을 붓스트랩 방법을 이용해서 구했다. 이때 존재하는 보조정보의 수준이 표본인 경우와 모집단인 경우로 나누어 모집단 총합에 대한 보정 추정 량(calibration estimator)을 구하고, 그에 따른 붓스트랩 분산 추 정량을 도출하였다. 또한 테일러 분산 추정량, 잭나이프 분산 추정량과 붓스트랩 분산 추정량의 효율성을 모의 실험을 통해 비교해 보았다. 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 anauxiliaIγ information having strong correlation with an interested variable in nonresponse situation. At this point, we find the 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 vanance estImators.