• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.923-936
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• 2021

A large number of non-responses are occurring in the sample survey, and various methods have been developed to deal with them appropriately. In particular, the bias caused by non-ignorable non-response greatly reduces the accuracy of estimation and makes non-response processing difficult. Recently, Chung and Shin (2017, 2020) proposed an estimator that improves the accuracy of estimation using parametric super-population model and response rate model. In this study, we suggested a bias corrected non-response mean estimator using a nonparametric function generalizing the form of a parametric super-population model. We confirmed the superiority of the proposed estimator through simulation studies.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.375-387
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• 2013

Unit non-response of surveys reduces the efficiency of the estimates and also causes non-response bias especially when there is large difference between respondents and non-respondents. Non-response weighting adjustments have usually been used to compensate for non-response. It is not easy to examine the non-response bias as well as to obtain information on the non-respondents in sample surveys. A household panel survey, called The Survey of Household Finances, was conducted in both 2010 and 2011. In this paper, we assume that non-response households in Wave 2 have strong non-response (non-cooperative) tendency. We classify those households into non-response households in Wave 1. Under this assumption, the characteristics of non-response households, the non-response bias and the effect of non-response adjustments are investigated.

• Survey Research
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• v.10 no.1
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• pp.169-186
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• 2009

The propensity score method is used to minimize the bias level in social survey, which comes from nonresponse. The theoretical concept and the background of the propensity score method is discussed first. The propensity score method was first applied in the epidemiology observational study. I have summarized the process of the three propensity score methods that were used to reduce estimation bias in this study. Matching by propensity score is applied to the relatively large control group. Subclassification has the advantage of using whole control group data and regression adjustment is applied to multiple covariates as well as propensity score of each unit is computable and usable. Lastly, the application procedures of propensity score method to reduce the nonresponse bias is suggested and its applicability to real situation is reviewed with the existing data.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1315-1329
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• 2009

The panel survey is need to have a more concern about a response due to a secession and non-response of a sample. And generally a population is not fixed and continuously changed. Thus, the rotation sample design can be used by the method replacing the panel research. This paper is the study of comparison to equal weight method, Duncan weight, Design weight method, weight share method in rotation sample design. More specifically, this paper compared variance estimators about the existing each method for the efficiency comparison, and to compare the precision using the relative efficiency gain by the Coefficient Variance(CV) after getting the design weight from the actual data.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.485-499
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• 2022

Controlling the total survey error including sampling error and non-sampling error is very important in sampling design. Non-sampling error caused by non-response accounts for a large proportion of the total survey error. Many studies have been conducted to handle non-response properly. Recently, a lot of non-response imputation methods using machine learning technique and traditional statistical methods have been studied and practically used. Most imputation methods assume MCAR(missing completely at random) or MAR(missing at random) and few studies have been conducted focusing on MNAR (missing not at random) or NN(non-ignorable non-response) which cause bias and reduce the accuracy of imputation. In this study, we propose a non-response imputation method that can be applied to non-ignorable non-response. That is, we propose an imputation method to improve the accuracy of estimation by removing the bias caused by NN. In addition, the superiority of the proposed method is confirmed through small simulation studies.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.631-642
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• 2019

Many methods have been developed to solve problems found in sample surveys involving a large number of item non-responses that cause inaccuracies in estimation. However, the non-response adjustment method used under the assumption of random non-response generates a bias in cases where the response rate is affected by the variable of interest. Chung and Shin (2017) and Min and Shin (2018) proposed a method to improve the accuracy of estimation by appropriately adjusting a bias generated when the response rate is a function of the variables of interest. In this study, we studied a case where the response rate function is linear and the error of the super population model follows normal distribution. We also examined the effect of the number of stratum population on bias adjustment. The performance of the proposed estimator was examined through simulation studies and confirmed through actual data analysis.

• Proceedings of the KAIS Fall Conference
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• 2007.11a
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• pp.57-59
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• 2007

정보보호시스템 평가수요의 예측은 예측된 평가수요를 근거로 적극적인 평가서비스를 제공하기 위하여 필요하다. 평가수요예측을 하기위하여 일반적으로 설문조사를 이용하지만 무응답 및 불성실한 응답으로 인하여 설문응답 자료만으로 평가수요를 예측하기에는 부족하다. 따라서 설문조사의 평가수요를 보정할 수 있는 모형이 필요하다. 본 논문에서는 설문조사를 통하여 예측 할 수 있는 직접적인 평가수요와 통계적 모형을 이용한 간접적 평가수요를 비교하고 설문조사의 대체 방법을 제시하였다.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.843-858
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• 2008

BLS adjustment methods have been able to provide more accurate estimates of total and make samples represent population characteristics by post-adjustment of design weights of samples. However, BLS methods use additional data, for instance number of employee, without this information or using other information, give different weight adjustment factors. In this paper we studied the sensitivity of the variables used in BLS adjustment. The 2007 monthly labor survey data is used in analysis.

• 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.

• 정보화사회
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• s.151
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• pp.58-61
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• 2001

한국정보통신산업협회에서는 지난 1995년부터 정부 정보통신부문 공식 통계 승인기관으로 정보통신부문 산업통계조사를 지속해 왔다. 본 실태조사는 2001년 3월 1일까지 교육인적자원부에 등록된 4년제 대학 186개교 중 2001년 4월 6일부터 5월 16일까지 정보통신기술인력의 기초 현황조사에 응한 정보통신부문 대학을 포함하는 143개 대학교를 대상으로 한 ‘정보통신기술인력 실태 조사’ 결과이다. 단, 무응답(non response)대학은 Sequetial Hot-Deck Imputation방법을 통해서 보정하였다.