Predicting missing values: a comparative study on non-parametric approaches for imputation

DOI10.1007/s00180-019-00900-3zbMath1505.62331OpenAlexW2961719033WikidataQ127729984 ScholiaQ127729984MaRDI QIDQ2282599

Markus Pauly, Burim Ramosaj

Publication date: 8 January 2020

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-019-00900-3

zbMATH Keywords

resamplingrandom forestMICEstochastic gradient tree boosting

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Missing data (62D10)

Related Items

Asymptotic‐based bootstrap approach for matched pairs with missingness in a single arm, Is there a role for statistics in artificial intelligence?, Unnamed Item

Uses Software

• R
• gbm
• C50
• MICE
• C4.5
• missForest
• BartPy

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