• 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.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.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.397-399
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• 2001

IETF에서는 차세대 인터넷 기술인 MPLS를 도입한 망에서의 다중경로 라우팅 및 부하 분산 방식으로서 MPLS OMP(Optimized MultiPath)를 제안하였다. 그런데, MPLS OMP는 다중 경로 집합을 계산하고 이 집합에 속하는 경로들의 활용률이 동일해 지는 것을 목표로 부하를 골고루 분산하기 때문에 긴 경로와 짧은 경로가 동일하게 활용되어 대역폭을 낭비할 수 있다는 단점이 있다. 이에 본 논문에서는 좀 더 MPLS 네트워크 자원을 효과적으로 사용하는 다중 경로 라우팅 방식을 제안하였다. 제안한 다중 경로 라우팅 방식은 최단경로의 활용률이 낮을때는 최단경로를 사용하고, 최단경로의 활용률이 높아지면 좀 더 길지만 활용률이 낮은 대체 경로 집합을 계산하여 대체 경로 집합의 경로들 중 흡수와 활용률을 반영해 무작위로 한 경로를 선택한다. 또한, 링크가 낭비되는 것을 막기 위해 링크의 활용률이 클수록 더 짧은 경로에 의해서만 사용되도록 제한한다. 그리고 계산한 대체 경로 집합의 활용률이 임계치 이상인 경우에는 대체 경로 집합의 크기를 늘린다. 시뮬레이션을 통하여 제한하는 방식과 단순한 최단 경로 방식을 비교한 결과, 제안하는 방식의 셀 손실률이 낮고, 연결 수락률이 높음을 볼 수 있었다.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.889-898
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• 2019

The problem of missing value imputation for variables in surveys that include item missing becomes complicated if outliers and logical boundary conditions between other survey items cannot be ignored. If there are outliers and boundaries in a variable including missing values, imputed values based on previous regression-based imputation methods are likely to be biased and not meet boundary conditions. In this paper, we approach these difficulties in imputation by combining various robust regression models and multiple imputation methods. Through a simulation study on various scenarios of outliers and boundaries, we find and discuss the optimal combination of robust regression and multiple imputation method.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.925-936
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• 2009

There very many approaches to impute missing values in the iid. case. However, it is hardly found the imputation techniques in the Markov random field(MRF) case. In this paper, we show that the imputation under MRF is just to impute by fitting the normal mixture model(NMM) under several practical assumptions. Our multivariate normal mixture model based approaches under MRF is applied to impute the missing pixel values of 3-variate (R, G, B) color image, providing a technique to smooth the imputed values.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.697-712
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• 2013

When analyzing repeated binary data, the generalized estimating equations(GEE) approach produces consistent estimates for regression parameters even if an incorrect working correlation matrix is used. However, time-varying covariates experience larger changes in coefficients than time-invariant covariates across various working correlation structures for finite samples. In addition, the GEE approach may give biased estimates under missing at random(MAR). Weighted estimating equations and multiple imputation methods have been proposed to reduce biases in parameter estimates under MAR. This article studies if the two methods produce robust estimates across various working correlation structures for longitudinal binary data with time-varying covariates under different missing mechanisms. Through simulation, we observe that time-varying covariates have greater differences in parameter estimates across different working correlation structures than time-invariant covariates. The multiple imputation method produces more robust estimates under any working correlation structure and smaller biases compared to the other two methods.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.83-97
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• 2019

As society develops, the dissemination of microdata has increased to respond to diverse analytical needs of users. Analysis of microdata for policy making, academic purposes, etc. is highly desirable in terms of value creation. However, the provision of microdata, whose usefulness is guaranteed, has a risk of exposure of personal information. Several methods have been considered to ensure the protection of personal information while ensuring the usefulness of the data. One of these methods has been studied to generate and utilize synthetic data. This paper aims to understand the synthetic data by exploring methodologies and precautions related to synthetic data. To this end, we first explain muptiple imputation, Bayesian predictive model, and Bayesian bootstrap, which are basic foundations for synthetic data. And then, we link these concepts to the construction of fully/partially synthetic data. To understand the creation of synthetic data, we review a real longitudinal synthetic data example which is based on sequential regression multivariate imputation.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.225-237
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• 2024

In the analysis of competing risks data, some of covariates may not be fully observed for some subjects. In such cases, excluding subjects with missing covariate values from the analysis may result in biased estimates and loss of efficiency. In this paper, we studied multiple imputation and the augmented inverse probability weighting method for regression parameter estimation in the cause-specific proportional hazards model with missing covariates. The performance of estimators obtained from multiple imputation and the augmented inverse probability weighting method is evaluated by simulation studies, which show that those methods perform well. Multiple imputation and the augmented inverse probability weighting method were applied to investigate significant risk factors for the risk of death from breast cancer and from other causes for breast cancer data with missing values for tumor size obtained from the Prostate, Lung, Colorectal, and Ovarian Cancer Screen Trial Study. Under the cause-specific proportional hazards model, the methods show that race, marital status, stage, grade, and tumor size are significant risk factors for breast cancer mortality, and stage has the greatest effect on increasing the risk of breast cancer death. Age at diagnosis and tumor size have significant effects on increasing the risk of other-cause death.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.119-119
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• 2015

스마트워터그리드의 목적은 기존 용수부족 문제의 근본적인 해결을 위해 새로운 취수원을 개발하고, 지역 내의 수자원을 효율적으로 활용하는 방안을 강구하는 시스템을 구축하는데 있다. 따라서 운영비용을 최소화하면서 수요처에 적정한 수량과 수질의 용수를 공급할 수 있도록 해야한다. 스마트워터그리드 구축 시 설비 비용에 대한 부담으로 보급 확산에 어려움을 겪을 가능성이 높으므로, 비용 효과적(Cost-effective)인 측면에서 스마트워터그리드의 경제성을 검토할 필요성이 있다. 본 연구에서는 자료의 한계로 인해 기존의 다중수원에 대한 경제성분석 사례를 인용하여 상수도 생산원가 절감액, 댐 건설과 관련된 비용의 감소, 물 오염 감소 등으로 편익을 간접적으로 계산하고, 기술적으로 물량이 충분하다는 가정하에 다중수원들간의 비용효과분석 수행하였다. 분석결과, 현재 공급하고 있는 상수도 시스템이 다른 다중취수원에 비해 비용효과적 우위에 있는 것으로 나타났다. 지하수, 해수담수화 등 새로운 수원을 찾는 노력이 필요하며 지하수는 가장 쉽게 활용할 수 있는 대체 수원이지만, 관정개발에 많은 비용이 들고 대량으로 수원을 공급하기 어렵다는 단점이 있다. 해수담수화는 대체수자원으로서 중요성이 더욱 강조될 것으로 보이지만, 시설의 설치 및 운영에 드는 높은 비용과 함께 육지에서 물을 대량으로 연안에서 멀리 떨어져 있거나 고도가 높은 지역은 해수담수화 기술의 적용이 어려울 것으로 여겨진다.