Results 21 to 30 of about 1,318,109 (301)
An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations [PDF]
BMC Genetics, 2011 Background Genome wide association studies (GWAS) are becoming the approach of choice to identify genetic determinants of complex phenotypes and common diseases.
Pereira Alexandre C +4 more
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Single Imputation Using Statistics-Based and K Nearest Neighbor Methods for Numerical Datasets
Ingénierie des Systèmes d'Information, 2023 A. Fadlil, Herman, Dikky Praseptian M
semanticscholar +3 more sources
Statistical Inference for Chi-square Statistics or F-Statistics Based on Multiple Imputation [PDF]
Missing data is a common issue in medical, psychiatry, and social studies. In literature, Multiple Imputation (MI) was proposed to multiply impute datasets and combine analysis results from imputed datasets for statistical inference using Rubin's rule. However, Rubin's rule only works for combined inference on statistical tests with point and variance ...
Binhuan Wang, Yixin Fang, Man Jin
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Advanced Statistics: Missing Data in Clinical Research--Part 2: Multiple Imputation [PDF]
Academic Emergency Medicine, 2007 In part 1 of this series, the authors describe the importance of incomplete data in clinical research, and provide a conceptual framework for handling incomplete data by describing typical mechanisms and patterns of censoring, and detailing a variety of relatively simple methods and their limitations.
Craig D. Newgard, Jason S. Haukoos
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Statistical inference with large‐scale trait imputation
Statistics in Medicine, 2023 Recently a nonparametric method called LS‐imputation has been proposed for large‐scale trait imputation based on a GWAS summary dataset and a large set of genotyped individuals. The imputed trait values, along with the genotypes, can be treated as an individual‐level dataset for downstream genetic analyses, including those that cannot be done with GWAS
Jingchen Ren, Wei Pan
openaire +2 more sources
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
doaj +1 more source
Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking [PDF]
Journal of Biomedical Informatics, 2023 The imputation of missing values in multivariate time series (MTS) data is a critical step in ensuring data quality and producing reliable data-driven predictive models.
Maksims Kazijevs, Manar D. Samad
semanticscholar +1 more source
Using machine learning to impute legal status of immigrants in the National Health Interview Survey
MethodsX, 2022 We describe a novel machine learning method of imputing legal status for immigrants using nationally representative survey data from the Survey of Income and Program Participation (SIPP) and the National Health Interview Survey (NHIS). K-nearest Neighbor
Simon A. Ruhnke +2 more
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CLUSTERING INCOMPLETE SPECTRAL DATA WITH ROBUST METHODS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 Missing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data.
S. Äyrämö +2 more
doaj +1 more source