• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.185-190
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• 2004

비모수적 결측치 대치 방법으로 널리 사용되는 k-nearest neighbors(KNN) 방법은 자료의 국소적(local) 특징을 고려하지 않고 전체 자료에 대해 균일한 이웃의 개수 k를 사용하는 단점이 있다. 본 연구에서는 KNN의 대안으로 자료의 국소적 특징을 고려하는 adaptive nearest neighbors(ANN) 방법을 제안하였다. 나아가 microarray 자료의 경우에 대하여 결측치 대치를 통해 KNN과 ANN의 성능을 비교하였다.

• Journal of KIISE:Software and Applications
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• v.32 no.2
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• pp.99-107
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• 2005

In this paper, wc propose a now missing imputation method for minimizing loss of information linkage disequilibrium-based and haplotype-based imputation method, which estimate missing values of the data based on the specificity of Single Nucleotide Polymorphism(SNP) genotype data. Method for imputing data is needed to minimize the loss of information caused by experimental missing data. In general, missing imputation of biological data has used major allele imputation method. but this approach is not optima]. 1'his method has high error rates of missing values estimation since the characteristics of the genotype data are not considered not take into consideration the specific structure of the data. In this paper, we show the results of the comparative evaluation of our model methods and major imputation method for the estimation of missing values.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.507-516
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• 2018

Widely used among the various single imputation methods is k-nearest neighbors (KNN) imputation due to its robustness even when a parametric model such as multivariate normality is not satisfied. We propose a weighted adaptive nearest neighbors imputation method that combines the adaptive nearest neighbors imputation method that accounts for the local features of the data in the KNN imputation method and weighted k-nearest neighbors method that are less sensitive to extreme value or outlier among k-nearest neighbors. We conducted a Monte Carlo simulation study to compare the performance of the proposed imputation method with previous imputation methods.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.585-594
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• 2009

Researchers of microarray experiment transpose processed images of raw data to possible data of statistical analysis: it is preprocessing. Preprocessing of microarray has image filtering, imputation and normalization. There have been studied about several different methods of normalization and imputation, but there was not further study on the order of the procedures. We have no further study about which things put first on our procedure between normalization and imputation. This study is about the identification of differentially expressed genes(DEG) on the order of the preprocessing steps using two-dye cDNA microarray in colon cancer and gastric cancer. That is, we check for compare which combination of imputation and normalization steps can detect the DEG. We used imputation methods(K-nearly neighbor, Baysian principle comparison analysis) and normalization methods(global, within-print tip group, variance stabilization). Therefore, preprocessing steps have 12 methods. We identified concordance measure of DEG using the datasets to which the 12 different preprocessing orders were applied. When we applied preprocessing using variance stabilization of normalization method, there was a little variance in a sensitive way for detecting DEG.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.23-31
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• 2015

A conventional missing value problem in the statistical analysis k-Nearest Neighbor(KNN) method are used for a simple imputation method. When one of the k-nearest neighbors is an extreme value or outlier, the KNN method can create a bias. In this paper, we propose a Weighted k-Nearest Neighbors(WKNN) imputation method that can supplement KNN's faults. A Monte-Carlo simulation study is also adapted to compare the WKNN method and KNN method using real data set.

• Journal of Life Science
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• v.28 no.11
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• pp.1255-1261
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• 2018

Whole genome analysis have been made possible with the development of DNA sequencing technologies and discovery of many single nucleotide polymorphisms (SNPs). Large number of SNP can be analyzed with SNP chips, since SNPs of human as well as livestock genomes are available. Among the various missing nucleotide imputation programs, Minimac3 software is suggested to be highly accurate, with a simplified workflow and relatively fast. In the present study, we used Minimac3 program to perform genomic missing value substitution 1,226 animals 770K SNP chip and imputing missing SNPs with next generation sequencing data from 311 animals. The accuracy on each chromosome was about 94~96%, and individual sample accuracy was about 92~98%. After imputation of the genotypes, SNPs with R Square ($R^2$) values for three conditions were 0.4, 0.6, and 0.8 and the percentage of SNPs were 91%, 84%, and 70% respectively. The differences in the Minor Allele Frequency gave $R^2$ values corresponding to seven intervals (0, 0.025), (0.025, 0.05), (0.05, 0.1), (0.1, 0.2), (0.2, 0.3). (0.3, 0.4) and (0.4, 0.5) of 64~88%. The total analysis time was about 12 hr. In future SNP chip studies, as the size and complexity of the genomic datasets increase, we expect that genomic imputation using Minimac3 can improve the reliability of chip data for Hanwoo discrimination.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.879-881
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• 2019

전자의무기록 (EMR)과 같은 의료 현장에서 수집되는 대용량의 데이터는 임상 해석적으로 잠재가치가 크고 활용도가 다양하나 결측값이 많아 희소성이 크다는 한계점이 있어 분석이 어렵다. 특히 EMR의 정보수집과정에서 발생하는 결측값은 무작위적이고 임의적이어서 분석 정확도를 낮추고 예측 모델의 성능을 저하시키는 주된 요인으로 작용하기 때문에, 결측치 대체는 필수불가결하다. 최근 통상적으로 활용되어지던 통계기반 알고리즘기반의 결측치 대체 알고리즘보다는 딥러닝 기술을 활용한 알고리즘들이 새로이 등장하고 있다. 본 논문에서는 Generative Adversarial Network를 기반한 최신 결측값 대치 알고리즘인 Generative Adversarial Imputation Nets을 적용하여 EMR에서의 성능을 분석해보고자 하였다.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1249-1257
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• 2011

In this paper, we propose a Sequential Adaptive Nearest Neighbor(SANN) imputation method that combines the Adaptive Nearest Neighbor(ANN) method and the Sequential k-Nearest Neighbor(SKNN) method. When choosing the nearest neighbors of missing observations, the proposed SANN method takes the local feature of the missing observations into account as well as reutilizes the imputed observations in a sequential manner. By using a Monte Carlo study and a real data example, we demonstrate the characteristics of the SANN method and its potential performance.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.4
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• pp.276-284
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• 2012

Classification algorithms generally assume that the data is complete. However, missing values are common in real data sets due to various reasons. In this paper, we propose to use locally linear reconstruction (LLR) for missing value imputation to improve the classification performance when missing values exist. We first investigate how much missing values degenerate the classification performance with regard to various missing ratios. Then, we compare the proposed missing value imputation (LLR) with three well-known single imputation methods over three different classifiers using eight data sets. The experimental results showed that (1) any imputation methods, although some of them are very simple, helped to improve the classification accuracy; (2) among the imputation methods, the proposed LLR imputation was the most effective over all missing ratios, and (3) when the missing ratio is relatively high, LLR was outstanding and its classification accuracy was as high as the classification accuracy derived from the compete data set.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.723-730
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• 2009

Missing values in time series can be treated as unknown parameters and estimated by maximum likelihood or as random variables and predicted by the expectation of the unknown values given the data. The purpose of this study is to impute missing values which are regarded as the maximum likelihood estimator and random variable in incomplete data and to compare with two methods using ARMA model. For illustration, the Mumps data reported from the national capital region monthly over the years 2001 ${\sim}$ 2006 are used, and results from two methods are compared with using SSF(Sum of square for forecasting error).