Results 21 to 30 of about 764,403 (277)
Methylation data imputation performances under different representations and missingness patterns
BMC Bioinformatics, 2020 Background High-throughput technologies enable the cost-effective collection and analysis of DNA methylation data throughout the human genome. This naturally entails missing values management that can complicate the analysis of the data.
Pietro Di Lena +3 more
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A survey on missing data in machine learning
Journal of Big Data, 2021 Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or ...
Tlamelo Emmanuel +5 more
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Evaluation of Multiple Imputation with Large Proportions of Missing Data: How Much Is Too Much?
Iranian Journal of Public Health, 2021 Background: Multiple Imputation (MI) is known as an effective method for handling missing data in public health research. However, it is not clear that the method will be effective when the data contain a high percentage of missing observations on a ...
Jin Hyuk Lee, J. Charles Huber Jr.
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BMC Medical Research Methodology, 2020 Background Longitudinal randomized controlled trials (RCTs) often aim to test and measure the effect of treatment between arms at a single time point. A two-sample χ2 test is a common statistical approach when outcome data are binary.
Mary L. Miller +3 more
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Analysis of Pregnancy and Other Factors on Detection of Human Papilloma Virus (HPV) Infection Using Weighted Estimating Equations for Follow-Up Data [PDF]
, 2000 Generalised estimating equations have been well established to draw inference for the marginal mean from follow-up data. Many studies suffer from missing data that may result in biased parameter estimates if the data are not missing completely at random.
Chang-Claude, J. +2 more
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Problems in dealing with missing data and informative censoring in clinical trials
Current Controlled Trials in Cardiovascular Medicine, 2002 A common problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to ...
Shih Weichung
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Multiple imputation with missing indicators as proxies for unmeasured variables: simulation study
BMC Medical Research Methodology, 2020 Background Within routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for example, in the case of electronic health records, where the presence or absence of data is informative.
Matthew Sperrin, Glen P. Martin
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BioData Mining, 2021 Background Missing data is a common issue in different fields, such as electronics, image processing, medical records and genomics. They can limit or even bias the posterior analysis.
Ben Omega Petrazzini +4 more
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Missing at random, likelihood ignorability and model completeness
The Annals of Statistics, 2004 This paper provides further insight into the key concept of missing at random (MAR) in incomplete data analysis. Following the usual selection modelling approach we envisage two models with separable parameters: a model for the response of interest and a model for the missing data mechanism (MDM).
Lu, Guobing, Copas, John B.
openaire +3 more sources
Ensaio, 2020 Almost all quantitative studies in educational assessment, evaluation and educational research are based on incomplete data sets, which have been a problem for years without a single solution.
Maria Eugénia Ferrão +2 more
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