Results 21 to 30 of about 179,516 (314)
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
Missing Data Imputation with High-Dimensional Data
The American Statistician, 2023 Imputation of missing data in high-dimensional datasets with more variables P than samples N, P≫N, is hampered by the data dimensionality. For multivariate imputation, the covariance matrix is ill conditioned and cannot be properly estimated. For fully conditional imputation, the regression models for imputation cannot include all the variables.
Alberto Brini, Edwin R. van den Heuvel
openaire +1 more source
Comparison of Performance of Data Imputation Methods for Numeric Dataset
Applied Artificial Intelligence, 2019 Missing data is common problem faced by researchers and data scientists. Therefore, it is required to handle them appropriately in order to get better and accurate results of data analysis.
Anil Jadhav +2 more
doaj +1 more source
JMIR mHealth and uHealth, 2020 BackgroundBody weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With
Turicchi, Jake +7 more
doaj +1 more source
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
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 +6 more sources
BMC Medical Research Methodology, 2020 Background Missing data are common in statistical analyses, and imputation methods based on random forests (RF) are becoming popular for handling missing data especially in biomedical research.
Shangzhi Hong, Henry S. Lynn
doaj +1 more source
Quality Control, Data Cleaning, Imputation
, 2023 This chapter addresses important steps during the quality assurance and control of RWD, with particular emphasis on the identification and handling of missing values. A gentle introduction is provided on common statistical and machine learning methods for imputation.
Liu, Dawei +4 more
openaire +6 more sources
Multiply-Imputed Synthetic Data: Advice to the Imputer [PDF]
Journal of Official Statistics, 2017 Abstract Several statistical agencies have started to use multiply-imputed synthetic microdata to create public-use data in major surveys. The purpose of doing this is to protect the confidentiality of respondents’ identities and sensitive attributes, while allowing standard complete-data analyses of microdata.
Loong, Bronwyn, Rubin, Donald B
openaire +2 more sources
Reuse of imputed data in microarray analysis increases imputation efficiency [PDF]
BMC Bioinformatics, 2004 Abstract Background The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set.
Kim, KY Kim, KY +2 more
openaire +4 more sources