Results 11 to 20 of about 175,631 (262)
IEEE Access, 2023 Missing data imputation is a technique to deal with incomplete datasets. Since many models and algorithms cannot be applied to data containing missing values, a pre-processing step needs to be performed to remove incomplete data or to estimate the ...
Reza Shahbazian, Sergio Greco
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Fairness in Missing Data Imputation
CoRR, 2021 Missing data are ubiquitous in the era of big data and, if inadequately handled, are known to lead to biased findings and have deleterious impact on data-driven decision makings. To mitigate its impact, many missing value imputation methods have been developed.
Yiliang Zhang, Qi Long
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To Impute or not to Impute? Missing Data in Treatment Effect Estimation
CoRR, 2022 Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missingness a particularly tricky endeavour. A key reason for this is that standard assumptions on missingness are rendered insufficient due to the presence of an additional ...
Jeroen Berrevoets +4 more
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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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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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Multiple imputation: dealing with missing data [PDF]
Nephrology Dialysis Transplantation, 2013 In many fields, including the field of nephrology, missing data are unfortunately an unavoidable problem in clinical/epidemiological research. The most common methods for dealing with missing data are complete case analysis-excluding patients with missing data--mean substitution--replacing missing values of a variable with the average of known values ...
Goeij, M.C.M. de +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 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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