• 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.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.603-608
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• 2024

Nonresponse and missing values are caused by sample dropouts and avoidance of answers to surveys. In this case, problems with the possibility of information loss and biased reasoning arise, and a replacement of missing values with appropriate values is required. In this paper, as an alternative to missing values imputation, we compare several replacement methods, which use mean, linear regression, random forest, K-nearest neighbor, autoencoder and denoising autoencoder based on deep learning. These methods of imputing missing values are explained, and each method is compared by using continuous simulation data and real data. The comparison results confirm that in most cases, the performance of the random forest imputation method and the denoising autoencoder imputation method are better than the others.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.407-426
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• 2003

We consider the missing covariates problem in generalized estimating equations(GEE) model. If the covariate is partially missing, GEE can not be calculated. In this paper, we study the performance of 7 imputation methods to handle missing covariates in GEE models, and the properties of GEE estimators are investigated after missing covariates are imputed for ordinal data of repeated measurements. The 7 imputation methods include i) Naive Deletion ii) Sample Average Imputation iii) Row Average Imputation iv) Cross-wave Regression Imputation v) Carry-over Imputation vi) Bayesian Bootstrap vii) Approximate Bayesian Bootstrap. A Monte-Carlo simulation is used to compare the performance of these methods. For the missing mechanism generating the missing data, we assume ignorable nonresponse. Furthermore, we generate missing covariates with or without considering wave nonresp onse patterns.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.47-48
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• 2020

데이터에 결측치가 존재할 때 어떤 데이터로 결측치를 대체시켜야 원래의 데이터에 가장 근접한 데이터를 만들어낼 수 있는지에 관한 연구. Z 값을 사용하면 평균으로 결측치를 대체시키는 것보다 더 정확한 결과를 도출해낼 수 있다.

• 주류공업
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• v.12 no.1
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• pp.39-43
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• 1992

• Childhood Kidney Diseases
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• v.18 no.2
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• pp.98-105
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• 2014

Purpose: Nafamostat mesilate (NM), a synthetic serine protease inhibitor, has been investigated as an anticoagulant for adult patients with a high risk of bleeding, who need chronic renal replacement therapy (CRRT). However, little is known about the use of NM as an anticoagulant in pediatric CRRT. The aim of this study was to evaluate the ideal dosage, efficacy, and safety of NM in pediatric CRRT. Methods: We conducted a retrospective study of 40 pediatric patients who had undergone at least 24 h of venovenous CRRTs between January 2011 and October 2013. We divided the patients according to risk of bleeding. Those at high risk received no anticoagulation (group 1) or NM as an anticoagulant (group 2), while those at low risk received heparin (group 3). Results: Forty patients (25 male and 15 female; mean age, $8.2{\pm}6.6$ years) were enrolled. The mean duration of CRRT was 13.0 days, and the survival rate was 57.5%. The mean hemofilter lifespan was 39.3 h in group 1 and 11.3 h in group 3. In group 2, hemofilter lifespan was extended from 7.5 h to 27.4 h after the use of NM (P =0.001). The mean hemofilter lifespan with NM was greater than with heparin (P =0.018). No patient experienced a major bleeding event during treatment with NM. Conclusion: NM may be a good alternative anticoagulant in pediatric patients with a high risk of bleeding requiring CRRT, and is not associated with bleeding complications.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.377-390
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• 2017

When conducting a survey, item nonresponse occurs if the respondent does not respond to some items. Since analysis based only on completely observed data may cause biased results, imputation is often conducted to analyze data in its complete form. The panel study is a survey method that examines changes of responses over time. In panel studies, there has been a preference for using information from response values of previous waves when the imputation of item nonresponses is performed; however, limited research has been conducted to support this preference. Therefore, this study compares the performance of imputation methods according to whether or not information from previous waves is utilized in the panel study. Among imputation methods that utilize information from previous responses, we consider ratio imputation, imputation based on the linear mixed model, and imputation based on the Bayesian linear mixed model approach. We compare the results from these methods against the results of methods that do not use information from previous responses, such as mean imputation and hot deck imputation. Simulation results show that imputation based on the Bayesian linear mixed model performs best and yields small biases and high coverage rates of the 95% confidence interval even at higher nonresponse rates.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.445-451
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• 2013

We consider the estimators for the parameters of the exponential model with limited replacements under the type I censoring scheme. Also, we propose the desirable number of replacements to provide the similar effects in terms of the mean square errors.

• Journal of the Speleological Society of Korea
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• v.21 no.22
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• pp.57-66
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• 1990

동굴의 습도는 년중 거의가 일정한 것이 특징이다. 그리고 캄캄한 암흑의 세계라는 점도 특색의 하나이다. 대체로 동굴의 대기는 그 기류의 움직임이 매우 느린 관계로 동구부근에는 대기온도와 동벽의 온도가 크게 달리 나타나지만 차차 거의가 비슷하게 나타난다. 이 때문에 종유석속 깊은 지점의 온도는 석회암의 온도와 관계되고 있으며 그 온도는 대체로 지표의 년간 평균기온과 비슷하다.(중략)