Results 1 to 10 of about 216,838 (308)
Agriculture, 2022 Imputation can be used to obtain a large number of high-density genotypes at the cost of procuring low-density panels. Accurate imputation requires a well-formed reference population of high-density genotypes to enable statistical inference. Five methods
Rudi A. McEwin +6 more
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Methods to Handle Incomplete Data
MAMC Journal of Medical Sciences, 2020 Context: The major question for data analysis is determining the appropriate analytic approach in the presence of incomplete observations. The most common solution to handle missing data in a data set is imputation, where missing values are estimated and
Vinny Johny +2 more
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Metabolites, 2020 To ensure scientific reproducibility of metabolomics data, alternative statistical methods are needed. A paradigm shift away from the p-value toward an embracement of uncertainty and interval estimation of a metabolite’s true effect size may lead to ...
Christopher E. Gillies +7 more
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Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches
Mathematics, 2021 Real-life data are bounded and heavy-tailed variables. Zero-one-inflated beta (ZOIB) regression is used for modelling them. There are no appropriate methods to address the problem of missing data in repeated bounded outcomes.
Urko Aguirre-Larracoechea +1 more
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K Nearest Neighbor Imputation Performance on Missing Value Data Graduate User Satisfaction
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2022 A missing value is a common problem of most data processing in scientific research, which results in a lack of accuracy of research results. Several methods have been applied as a missing value solution, such as deleting all data that have a missing ...
Abdul Fadlil, Herman, Dikky Praseptian M
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Outcome-sensitive multiple imputation: a simulation study
BMC Medical Research Methodology, 2017 Background Multiple imputation is frequently used to deal with missing data in healthcare research. Although it is known that the outcome should be included in the imputation model when imputing missing covariate values, it is not known whether it should
Evangelos Kontopantelis +3 more
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Applied Sciences, 2022 The aim of this study is to ascertain the most suitable model for predicting complex odors using odor substance data that has a small number of data and a large number of missing data.
Do-Hyun Lee +3 more
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Bayesian Estimation of Disclosure Risks for Multiply Imputed, Synthetic Data
The Journal of Privacy and Confidentiality, 2014 Agencies seeking to disseminate public use microdata, i.e., data on individual records, can replace confidential values with multiple draws from statistical models estimated with the collected data.
Jerome P. Reiter +2 more
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Integration of Multimodal Data from Disparate Sources for Identifying Disease Subtypes
Biology, 2022 Studies over the past decade have generated a wealth of molecular data that can be leveraged to better understand cancer risk, progression, and outcomes.
Kaiyue Zhou +5 more
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Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population
Frontiers in Genetics, 2019 Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by ...
Haiko Schurz +9 more
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