Results 21 to 30 of about 318,466 (281)

Comparison of multiple imputation and other methods for the analysis of imputed genotypes

BMC Genomics, 2023
Abstract Background Analysis of imputed genotypes is an important and routine component of genome-wide association studies and the increasing size of imputation reference panels has facilitated the ability to impute and test low-frequency variants for associations. In the context of genotype imputation, the true genotype
Paul L. Auer, Gao Wang, Guangyou Li, Andrew T. DeWan, Suzanne M. Leal   +4 more
openaire   +3 more sources

Multiple Imputation Ensembles (MIE) for dealing with missing data [PDF]

, 2020
Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation ...
A Farhangfar, AM Sefidian, B Schölkopf, C Cortes, CT Tran, DA Newman, DB Rubin, DB Rubin, DH Wolpert, EL Silva-Ramírez, GE Batista, GJ van der Heijden, H Gao, IH Witten, J Demšar, J Honaker, J Honaker, J Scheffer, JA Sterne, JL Schafer, JL Schafer, JR Quinlan, K Abayomi, KM Ting, L Breiman, L Breiman, L Rokach, M Fichman, M Khalilia, M Spratt, MA Klebanoff, MJ Azur, NJ Horton, PJ García-Laencina, PJ Kelly, PN Tan, RJ Little, S García, S Van Buuren, S Van Buuren, SS Chae, SS Choi, U Garciarena, V Vapnik, X Chen, Y Dong, Y Freund, Y He, Z Che, Z Liu   +49 more
core   +1 more source

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, Jiatao Gan, Changfeng Han, Yanbing Guo, Kaihao Chen, Ya-zhou Shi, Ben-gong Zhang   +6 more
doaj   +1 more source

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, Leizhen Xia, Qimeng Zhang, Shaoning Wu, Mingcheng Wu, Hongbo Liu   +5 more
doaj   +1 more source

A method for increasing the robustness of multiple imputation [PDF]

Computational Statistics & Data Analysis, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rhian M. Daniel, Michael G. Kenward
openaire   +2 more sources

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, Zhang Zhe, Ding Xiangdong, Fu Weixuan, Ma Peipei, Wang Chonglong, Zhang Qin   +6 more
doaj   +1 more source

Informer-WGAN: High Missing Rate Time Series Imputation Based on Adversarial Training and a Self-Attention Mechanism

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, Limei Tian, Baichen Zhai, Shufan Zhang, Rui Wu   +4 more
doaj   +1 more source

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
core   +2 more sources

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, Tandi E Matsha, Rajiv T Erasmus, Andre P Kengne   +3 more
doaj   +1 more source

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
doaj   +1 more source