• Communications for Statistical Applications and Methods
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• 제17권6호
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• pp.899-907
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• 2010

무응답 대체(non-response imputation) 방법에 관한 많은 이론과 방법이 제안되었으며 실제 자료 분석에 이용되고 있다. 흔히 횡단면 무응답 대체를 위하여 다중대체법(multiple imputation)이 사용되고 있으며 2차년도 이상의 패널자료에는 종시점회귀대체법(cross-wave regression imputation)이 사용되고 있다. 본 연구에서는 패널자료 분석을 위하여 종시점회귀대체법의 일반형태인 시계열 대체법과 횡단면 무응답 대체법을 결합한 시계열-횡단면 다중 대체법을 제안하였다. 노동부의 매월노동통계 자료를 이용하여 제안한 방법과 기존의 종시점회귀대체법을 비교하여 우수함을 보였다.

• Communications for Statistical Applications and Methods
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• 제30권6호
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• pp.577-587
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• 2023

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.

• Journal of Information Processing Systems
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• 제9권4호
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• pp.633-650
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• 2013

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.

• Journal of the Korean Statistical Society
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• 제36권3호
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• pp.423-434
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• 2007

Imputation using a regression model is a method to preserve the correlation among variables and to provide imputed point estimators. We discuss the implementation of regression imputation using fractional imputation. By a suitable choice of fractional weights, the fractional regression imputation can take the form of hot deck fractional imputation, thus no artificial values are constructed after the imputation. A variance estimator, which extends the method of Kim and Fuller (2004), is also proposed. Results from a limited simulation study are presented.

• Communications for Statistical Applications and Methods
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• 제30권3호
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• pp.331-341
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• 2023

Handling missing values in data analysis is essential in constructing a good prediction model. The easiest way to handle missing values is to use complete case data, but this can lead to information loss within the data and invalid conclusions in data analysis. Imputation is a technique that replaces missing data with alternative values obtained from information in a dataset. Conventional imputation methods include K-nearest-neighbor imputation and multiple imputations. Recent methods include missForest, missRanger, and mixgb ,all which use machine learning algorithms. This paper compares the imputation techniques for datasets with mixed datatypes in various situations, such as data size, missing ratios, and missing mechanisms. To evaluate the performance of each method in mixed datasets, we propose a new imputation performance measure (IPM) that is a unified measurement applicable to numerical and categorical variables. We believe this metric can help find the best imputation method. Finally, we summarize the comparison results with imputation performances and computational times.

• 응용통계연구
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• 제31권4호
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• pp.507-516
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• 2018

결측치를 대치하는 여러가지 단일대치법 중에서 다변량 정규성 등의 모수적 모형이 만족되지 않을 때에도 강건성(robustness)을 지니는 k-최근접 이웃 대치법(k-nearest neighbors; KNN)이 널리 활용된다. KNN대치법에서 자료의 국소적 특징을 반영한 적응 최근접 이웃(adaptive nearest neighbors; ANN) 대치법과 k개의 최근접 이웃들 중 극단값이나 이상값이 있는 경우 이들의 영향에 덜 민감한 가중 k-최근접 이웃(weighted KNN; WKNN) 대치법의 장점을 결합한 가중 적응 최근접 이웃(weighted ANN; WANN) 대치법을 제안하였다. 또한 모의실험을 통하여 기존의 방법들과 제안한 방법을 비교하였다.

• Journal of the Korean Statistical Society
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• 제29권4호
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• pp.385-394
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• 2000

Jackknife variance estimation based on adjusted imputed values when nonresponse is nonrandom and follow-up data are available for a subsample of nonrespondents is provided. Both hot-deck and ratio imputation method are considered as imputation method. The performance of the proposed variance estimator under nonrandom response mechanism is investigated through numerical simulation.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권2호
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• pp.99-107
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• 2005

본 논문에서는 단일염기변이(SNP: Single Nucleotide Polymorphism)와 같은 유전형(Rcnotype)자료에서 결측치가 발생하였을 경우 유전형 자료의 특이성을 고려해 자료 원래의 정보손실을 최소화하는 대치법인 연관불균형 기반의 대치법(linkage disequilibrium- based imputation)과 일배체형 기반의 대치법(haplotype-based imputation)을 제시한다. 이러한 결측치 대치는 실험상에서 발생하는 결측치에 의한 중요한 정보의 손실을 최소화 한다는 점에서 필요한 방법이다. 일반적으로 그동안 생물학 자료의 결측치 대치는 대부분 주형질 대치법(major allele imputation)이 활용되어왔는데 유전형 자료에서의 이 방법의 사용은 사료의 특이성으로 인하여 결측치에 대한 높은 오차율(error rate)을 보임으로서 자료의 신뢰성을 떨어뜨릴 수 있다. 본 논문에서는 유전형 자료인 단일염기변이 자료의 시뮬레이션을 통하여 기존의 주형질 대치법과 논문에서 제안된 연관불균형 기반의 대치법과 일배체형 기반의 대치법을 비교하고 그 결과를 보여 준다.

• 응용통계연구
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• 제21권4호
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• pp.659-664
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• 2008

Missing continuous covariates are pervasive in the use of generalized linear models for medical data. Multiple imputation is the most common and easy-to-do method of dealing with missing covariate data. However, there are always serious warnings in using this method. There should be concern to make imputed values more proper. In this paper, proper imputation from posterior predictive distribution is developed for implementing with arbitrary priors. We use empirical distribution of the posterior for approximating the posterior predictive distribution, to sample from it. This method is preferable in comparison with a presented imputation method of us which uses a full model to impute missing values using available software. The proposed methods are implemented on glucocorticoid data.

• Journal of the Korean Data and Information Science Society
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• 제15권4호
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• pp.945-955
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• 2004

'Complete-case analysis' is easy to carry out and it may be fine with small amount of missing data. However, this method is not recommended in general because the estimates are usually biased and not efficient. There are numerous alternatives to complete-case analysis. One alternative is the single imputation. Some of the most common single imputation methods are reviewed and the performances are compared by simulation studies.