Results 11 to 20 of about 287,343 (334)
DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation
Information, 2022 Insights and analysis are only as good as the available data. Data cleaning is one of the most important steps to create quality data decision making. Machine learning (ML) helps deal with data quickly, and to create error-free or limited-error datasets.
Reza Shahbazian, Irina Trubitsyna
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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 +49 more
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Multiple imputation with missing data indicators [PDF]
Statistical Methods in Medical Research, 2021 Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation, also called chained equations multiple imputation. In this approach, we impute missing values using regression models for each variable, conditional on the ...
Lauren J Beesley +5 more
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Informatics, 2023 The Health and Aging Brain Study–Health Disparities (HABS–HD) project seeks to understand the biological, social, and environmental factors that impact brain aging among diverse communities. A common issue for HABS–HD is missing data. It is impossible to
Fan Zhang +6 more
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A Comparison of Different Methods for Rainfall Imputation: A Galician Case Study
Applied Sciences, 2023 With the ultimate goal of developing models that involve the use of environmental variables, a GIS-based application is being developed that is circumscribed to the region of Galicia (Spain). Ten-minute data of six meteorological variables were collected
José Vidal-Paz +2 more
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Multiple imputation of maritime search and rescue data at multiple missing patterns.
PLoS ONE, 2021 Based on the missing situation and actual needs of maritime search and rescue data, multiple imputation methods were used to construct complete data sets under different missing patterns.
Guobo Wang +4 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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Missing data, imputation, and endogeneity [PDF]
Journal of Econometrics, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
McDonough, Ian K., Millimet, Daniel L.
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Impute the Missing Data Using Retrieved Dropouts [PDF]
BMC Medical Research Methodology, 2021 Abstract Background: In the past few decades various methods have been proposed to handle missing data of clinical studies, so as to assess the robustness of primary results. Some of the methods are based on the assumption of missing at random (MAR) which assumes subjects who discontinue the treatment will maintain the treatment effect after ...
Shuai Wang, Haoyan Hu
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A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records
Entropy, 2022 Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing.
Shivani Batra +5 more
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