Results 31 to 40 of about 313,820 (280)
Investigating Some Imputation Methods of Multivariate Imputation Chained Equations
European Journal of Mathematics and Statistics, 2022 This paper investigates three MICE methods: Predictive Mean Matching (PMM), Quantile Regression-based Multiple Imputation (QR-basedMI) and Simple Random Sampling Imputation (SRSI) at imputation numbers 5, 15, 20 and 30 with 5% and 20% missing values, to ascertain the one that produces imputed values that best matches the observed values and compare the
M. T. Nwakuya, E. O. Biu
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Imputation Methods for scRNA Sequencing Data
Applied Sciences, 2022 More and more researchers use single-cell RNA sequencing (scRNA-seq) technology to characterize the transcriptional map at the single-cell level. They use it to study the heterogeneity of complex tissues, transcriptome dynamics, and the diversity of ...
Mengyuan Wang +6 more
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The ability of different imputation methods for missing values in mental measurement questionnaires
BMC Medical Research Methodology, 2020 Background Incomplete data are of particular important influence in mental measurement questionnaires. Most experts, however, mostly focus on clinical trials and cohort studies and generally pay less attention to this deficiency. We aim is to compare the
Xueying Xu +5 more
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Application of imputation methods to genomic selection in Chinese Holstein cattle
Journal of Animal Science and Biotechnology, 2012 Missing genotypes are a common feature of high density SNP datasets obtained using SNP chip technology and this is likely to decrease the accuracy of genomic selection.
Weng Ziqing +6 more
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A comparison of imputation methods for categorical data
Informatics in Medicine Unlocked, 2023 Objectives: Missing data is commonplace in clinical databases, which are being increasingly used for research. Without giving any regard to missing data, results from analysis may become biased and unrepresentative.
Shaheen MZ. Memon +2 more
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Algorithms, 2022 Missing observations in time series will distort the data characteristics, change the dataset expectations, high-order distances, and other statistics, and increase the difficulty of data analysis.
Yufan Qian +4 more
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On the regression method of estimation of population mean from incomplete survey data through imputation [PDF]
, 2005 When some observations in the sample data are missing, the application of the regression method is considered for the estimation of population mean with and without the use of imputation.
Shalabh, Toutenburg, Helge
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Imputation with the R Package VIM
Journal of Statistical Software, 2016 The package VIM (Templ, Alfons, Kowarik, and Prantner 2016) is developed to explore and analyze the structure of missing values in data using visualization methods, to impute these missing values with the built-in imputation methods and to verify the ...
Alexander Kowarik, Matthias Templ
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Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa. [PDF]
PLoS ONE, 2015 Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing ...
Katya L Masconi +3 more
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SnapFISH-IMPUTE: an imputation method for multiplexed DNA FISH data
Communications BiologyAbstractChromatin spatial organization plays a crucial role in gene regulation. Recently developed and prospering multiplexed DNA FISH technologies enable direct visualization of chromatin conformation in the nucleus. However, incomplete data caused by limited detection efficiency can substantially complicate and impair downstream analysis.
Hongyu Yu +6 more
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