Results 11 to 20 of about 161,037 (263)
Missing Data Imputation for Supervised Learning [PDF]
Applied Artificial Intelligence, 2018 Missing data imputation can help improve the performance of prediction models in situations where missing data hide useful information. This paper compares methods for imputing missing categorical data for supervised classification tasks.
Jason Poulos, Rafael Valle
doaj +3 more sources
missForestPredict-Missing data imputation for prediction settings. [PDF]
PLoS OnePrediction models are used to predict an outcome based on input variables. Missing data in input variables often occur at model development and at prediction time. The missForestPredict R package proposes an adaptation of the missForest imputation algorithm that is fast, user-friendly and tailored for prediction settings.
Albu E, Gao S, Wynants L, Van Calster B.
europepmc +5 more sources
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.
openaire +3 more sources
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
openaire +2 more sources
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
openaire +4 more sources
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
openaire +3 more sources
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.
doaj +1 more source
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
openaire +7 more sources
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
doaj +1 more source
MIAEC: Missing Data Imputation Based on the Evidence Chain
IEEE Access, 2018 Missing or incorrect data caused by improper operations can seriously compromise security investigation. Missing data can not only damage the integrity of the information but also lead to the deviation of the data mining and analysis.
Xiaolong Xu +4 more
doaj +1 more source