• 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.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.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.143-148
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• 2003

표본조사에서 발생되는 무응답에 대한 대체법은 매우 다양하게 연구 되고 있다. 특히 모형을 기반으로 하는 회귀 대체법은 매우 활용도가 높다. 이 때 일반적으로 종속변수가 결측값의 변수가 되며 독립변수는 주어지게 된다. 주어지 주어진 종속변수와 독립변수의 값을 이용하여 모델을 설정하고 그에 따라 결측값을 예측하여 대체하게 된다. 이 때 예측값 즉 결측값을 구하는 과정에서 독립변수 값 자체에도 결측값이 생기게 된다는 것이다. 이때 여러 가지 방법으로 독립변수의 결측값을 대체하고 모형을 활용할 수 있다. 그러나 이 연구에서는 독립변수들을 같은 특성끼리 그룹화 시키는 요인분석(factor analysis)을 이용하여 독립변수의 결측값에따른 예측된 결측값의 변동을 최소화 하고자했다.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.95-107
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• 2008

Survey on the rural living indicators was the statistic approved from National Statistical Office and the survey executed by rural resources development institute. This study was used the raw data of survey on the rural living indicators in 2005. After editing procedure for raw data, we were studied 1,582 households which is acquired through elimination of case included nonresponses, and imputed a nonresponses of 15 item selected from 146 item. The imputation methods and efficiency of imputation for simulation was adapted differently from type of data. For continuous data, we imputed the nonresponses with mean imputation, regression imputation, adjusted grey-based k-NN imputation(DU, DW, WU, WW) and compared the results with RMSE. For categorical data, we imputed the nonresponses with mode method, probability imputation, conditional mode method, conditional probability method, hot-deck imputation, and compared the results with Accuracy. By the results, regression imputation and adjusted grey-based k-NN imputation appropriated for continuous data and hot-deck imputation appropriated for categorical data.

• 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.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.367-376
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• 2011

BLS weight adjustment is a widely used method for business surveys with non-responses and outliers. Recent surveys show that the non-response weight adjustment of the BLS method is the same as the ratio imputation method. In this paper, we suggested a modified BLS weight adjustment method by imputing missing values instead of using weight adjustment for non-response. Monthly labor survey data is used for a small Monte-Carlo simulation and we conclude that the suggested method is superior to the original BLS weight adjustment method.

• 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.

• 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.

• Journal of the Korean Statistical Society
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• v.8 no.2
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• pp.109-116
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• 1979

두 개의 변수의 대체효과(substitution effect)를 연구하기 위하여 수요 또는 공급의 모형을 만들었을 경우 이에 관련된 변수들의 이름이 중요시 된다. 실제 관측 자료를 사용하였을 경우 흔히 일어나는 다공선성(multicollinearity) 문제를 다루기 위한 대안으로써 선형회귀선을 예로 들어 능형회귀기법(ridge regression technique)과 요인분석기법(factor analytic technique)을 소개하였으며 이에서 얻어지는 계수(coefficient)를 OLS 추정치로 설명하기 위하여 원래의 자료를 변환하였다. 실지 수요와 공급의 모형이 비선형일 경우 일반적으로 능형회귀나 요인분석을 쓰지 못한다는 점을 감안, 이러한 방법을 자료의 변환방법으로 설명함으로써 비선형모형에서도 다공선성문제를 위하여 능형회귀분석법이나 요인분석기법을 사용할 수 있도록 하였다.