• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.597-606
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• 2005

The nonresponse in sample survey causes a problem when it comes time to analyze dataset in public-use files where the user has only complete-data methods available and has limited information about the reasons for nonresponse. Recently imputation for nonresponse is becoming a standard approach for handling nonresponse and various imputation methods have been devised . However, most imputation methods concern with continuous traits while many interesting features are measured by binary or ordered categorical scales in sample survey. In this note. an imputation method for ignorable nonresponse in binary or ordered categorical traits is considered.

• Survey Research
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• v.11 no.1
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• pp.1-18
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• 2010

We compare various imputation methods for compensating wave nonresponse that are commonly adopted in many panel surveys. Unlike the cross-sectional survey, the panel survey is involved a time-effect in nonresponse in a sense that nonresponse may happen for some but not all waves. Thus, responses in neighboring waves can be used as powerful predictors for imputing wave nonresponse such as in longitudinal regression imputation, carry-over imputation, nearest neighborhood regression imputation and row-column imputation method. For comparison, we carry out a simulation study on a few income data from the Korean Welfare Panel Study based on two performance criteria: predictive accuracy and estimation accuracy. Our simulation shows that the ratio and row-column imputation methods are much more effective in terms of both criteria. Regression, longitudinal regression and carry-over imputation methods performed better in predictive accuracy, but less in estimation accuracy. On the other hand, nearest neighborhood, nearest neighbor regression and hot-deck imputation show higher performance in estimation accuracy but lower predictive accuracy. Finally, the mean imputation shows much lower performance in both criteria.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.899-907
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• 2010

Several non-response imputation methods are suggested, however, mainly cross-sectional imputations are studied and applied to this analysis. A simple and common imputation method for panel data is the cross-wave regression imputation or carry-over imputation as a special case of cross-wave regression imputation. This study suggests a multiple imputation method combined time series analysis and cross-sectional multiple imputation method. We compare this method and the cross-wave regression imputation method using MSE, MAE, and Bias. The 2008 monthly labor survey data is used for this study.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1229-1241
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• 2014

Most of sampling surveys have outliers and non-response missing values simultaneously. In that case, due to the effect of outliers, the result of imputation is not good enough to meet a given precision. To overcome this situation, outlier treatment should be conducted before imputation. In this paper in order for reducing the effect of outlier, we study outlier imputation methods and outlier weight adjustment methods. For the outlier detection, the method suggested by She and Owen (2011) is used. A small simulation study is conducted and for real data analysis, Monthly Labor Statistic and Briquette Consumption Survey Data are used.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.909-921
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• 2010

Cross-wave regression imputation and carry-over imputation method are generally used in the analysis of panel data with missing values. Recently it is known that the BLS non-response adjust method has good statistical properties. In this paper we show that the BLS method can be considered as an imputation method with a similar formula of a ratio-estimator. In addition, we show that the carry-over imputation and BLS imputation are approximately the same under the assumption that data follow a non-stationary process with drift. Small simulation studies and real data analysis are performed. For the real data analysis, a monthly labor statistic (2007) is used.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.47-55
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• 2011

Ratio and regression imputations depend on the model of a survey variable and the relation between the survey variable and auxiliary variables. If the model is not true, the unbiasedness of the estimator using the ratio or regression imputation cannot be guaranteed. In this paper, we develop the doubly robust imputation, which satisfies the approximate unbiasedness of the estimator, whether the model assumption is valid or not. The proposed imputation increases the efficiency of estimation by using the population information of the auxiliary variables. The simulation study establishes the theoretical results of this paper.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.800-800
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• 2012

수자원사업에 따른 수질개선편익은 사업으로 인해 저감되는 오염원의 양을 예측하고, 그에 따른 수질개선효과를 계량화한 값이며, 이는 곧 해당사업의 가치로써 정의된다. 이러한 수질개선편익을 산정하기 위한 방법에는 개인의 지불의사를 추정하여 가치를 추정하는 조건부가치측정법(CVM)과 해당 사업에 따른 효과를 기술적 방식으로 대체할 때 소요되는 비용을 시행사업의 가치로 평가하는 방식인 대체비용법을 들 수 있다. 현재 조건부가치측정법은 다양한 분야의 사업에 대한 가치추정법으로 널리 쓰이고 있으나, 해당사업의 가치에 대한 공학적이고 직관적인 해답을 줄 수 있는 대체비용법의 적용성은 아직까지 미미한 실정이다. 이에 본 연구는 수질개선편익을 중심으로 대체비용법의 분석절차와 적용성을 확인하고자 하였고, 이에 대한 사례연구로서 댐증고사업을 대상으로 하였다. 수질개선편익과 같이 환경적 질을 높이는 사업은 대상사업의 가치를 추정하는 데 많은 어려움이 있으며, 기존에 주로 사용된 조건부가치측정법과 더불어 대체비용법을 이용한 검토는 보다 객관적인 의사결정을 지원하는 데 도움을 줄 것으로 판단된다.

• 월간산업보건
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• s.358
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• pp.42-46
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• 2018

• The Korean Society of Law and Medicine
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• v.6 no.1
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• pp.153-164
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• 2005

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.635-647
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• 2013

Imputation is a commonly used method to handle missing survey data. The performance of the imputation method is influenced by various factors, especially an outlier. The removal of the outlier in a data set is a simple and effective approach to reduce the effect of an outlier. In this paper in order to improve the precision of multiple imputation, we study a imputation method which reduces the effect of outlier using various weight adjustment methods that include the removal of an outlier method. The regression method in PROC/MI in SAS is used for multiple imputation and the obtained final adjusted weight is used as a weight variable to obtain the imputed values. Simulation studies compared the performance of various weight adjustment methods and Monthly Labor Statistic data is used for real data analysis.