• Journal of Animal Science and Technology
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• v.58 no.1
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• pp.1.1-1.6
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• 2016

Background: Genotype imputation is an important process of predicting unknown genotypes, which uses reference population with dense genotypes to predict missing genotypes for both human and animal genetic variations at a low cost. Machine learning methods specially boosting methods have been used in genetic studies to explore the underlying genetic profile of disease and build models capable of predicting missing values of a marker. Methods: In this study strategies and factors affecting the imputation accuracy of parent-offspring trios compared from lower-density SNP panels (5 K) to high density (10 K) SNP panel using three different Boosting methods namely TotalBoost (TB), LogitBoost (LB) and AdaBoost (AB). The methods employed using simulated data to impute the un-typed SNPs in parent-offspring trios. Four different datasets of G1 (100 trios with 5 k SNPs), G2 (100 trios with 10 k SNPs), G3 (500 trios with 5 k SNPs), and G4 (500 trio with 10 k SNPs) were simulated. In four datasets all parents were genotyped completely, and offspring genotyped with a lower density panel. Results: Comparison of the three methods for imputation showed that the LB outperformed AB and TB for imputation accuracy. The time of computation were different between methods. The AB was the fastest algorithm. The higher SNP densities resulted the increase of the accuracy of imputation. Larger trios (i.e. 500) was better for performance of LB and TB. Conclusions: The conclusion is that the three methods do well in terms of imputation accuracy also the dense chip is recommended for imputation of parent-offspring trios.

• 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.

• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1232-1246
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• 2021

Recently, the cattle genome sequence has been completed, followed by developing a commercial single nucleotide polymorphism (SNP) chip panel in the animal genome industry. In order to increase statistical power for detecting quantitative trait locus (QTL), a number of animals should be genotyped. However, a high-density chip for many animals would be increasing the genotyping cost. Therefore, statistical inference of genotype imputation (low-density chip to high-density) will be useful in the animal industry. The purpose of this study is to investigate the effect of the reference population size and marker density on the imputation accuracy and to suggest the appropriate number of reference population sets for the imputation in Hanwoo cattle. A total of 3,821 Hanwoo cattle were divided into reference and validation populations. The reference sets consisted of 50k (38,916) marker data and different population sizes (500, 1,000, 1,500, 2,000, and 3,600). The validation sets consisted of four validation sets (Total 889) and the different marker density (5k [5,000], 10k [10,000], and 15k [15,000]). The accuracy of imputation was calculated by direct comparison of the true genotype and the imputed genotype. In conclusion, when the lowest marker density (5k) was used in the validation set, according to the reference population size, the imputation accuracy was 0.793 to 0.929. On the other hand, when the highest marker density (15k), according to the reference population size, the imputation accuracy was 0.904 to 0.967. Moreover, the reference population size should be more than 1,000 to obtain at least 88% imputation accuracy in Hanwoo cattle.

• 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.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.273-282
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• 2009

Missing data is one of the common problems in building analysis or prediction models using software project data. Missing imputation methods are known to be more effective missing data handling method than deleting methods in small software project data. While K nearest neighbor imputation is a proper missing imputation method in the software project data, it cannot use non-missing information of incomplete project instances. In this paper, we propose an approach to missing data imputation for numerical software project data by combining K nearest neighbor and maximum likelihood estimation; we also extend the average absolute error measure by normalization for accurate evaluation. Our approach overcomes the limitation of K nearest neighbor imputation and outperforms on our real data sets.

• Proceedings of the Korean Association for Survey Research Conference
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• 2000.06a
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• pp.1-14
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• 2000

We consider statistical methods for nonresponse problem in social and economic sample surveys. To create a complete data set, which does not include item nonresponse data, imputation methods are generally used. In this paper, we introduce some imputation methods and compare them with one another. Also, we consider some problems, which occur when an imputed data set is treated as a response data set. Due to the imputed values, the true variance of the estimator after imputation is increased by the imputation variance. However, since usual naive variance estimator constructed from the imputed data set does not estimate the imputation variance, the true variance of the estimator after imputation tends to be underestimated. Theoretical reason is investigated and serious results are explained through a simulation study. Finally, some adjusted variance estimation methods to compensate for underestimation are presented and discussed.

• Survey Research
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• v.1 no.2
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• pp.1-14
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• 2000

We consider statistical methods for nonresponse problem in social and economic sample surveys. To create a complete data set, which does not include item nonresponse data, imputation methods are generally used. In this paper, we introduce some imputation methods and compare them with one another. Also, we consider some problems, which occur when an imputed data set is treated as a response data set. Due to the imputed values, the true variance of the estimator after imputation is increased by the imputation variance. However, since usual naive variance estimator constructed from the imputed data set does not estimate the imputation variance, the true variance of the estimator after imputation tends to be underestimated. Theoretical reason is investigated and serious results are explained through a simulation study. Finally, some adjusted variance estimation methods to compensate for underestimation are presented and discussed.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.183-188
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• 2002

Multiple imputation, proposed by Rubin, is a procedure for handling missing data. One of the attractive parts of multiple imputation is the simplicity of the variance estimation formula. Because of the simplicity, it has been often abused and misused beyond its original prescription. This paper provides the bias of the multiple imputation variance estimator for a linear point estimator and discusses when the bias can be safely neglected.

• Genomics & Informatics
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• v.15 no.3
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• pp.108-111
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• 2017

Recently developed computational methods allow the imputation of human leukocyte antigen (HLA) genes using intergenic single nucleotide polymorphism markers. To improve the imputation accuracy in HLA imputation, it is essential to increase the sample size and the diversity of alleles in the reference panel. Our software, MergeReference, helps achieve this goal by providing a streamlined pipeline for combining multiple reference panels into one.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.441-450
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• 2003

Collaborative filtering is one of the most widely used methodologies for recommendation system. Collaborative filtering is based on a data matrix of each customer's preferences and frequently, there exits missing data problem. We introduced two imputation approach (multiple imputation via Markov Chain Monte Carlo method and multiple imputation via bootstrap method) to improve the prediction performance of collaborative filtering and evaluated the performance using EachMovie data.