Predicting missing values: a comparative study on non-parametric approaches for imputation
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Publication:2282599
DOI10.1007/s00180-019-00900-3zbMath1505.62331OpenAlexW2961719033WikidataQ127729984 ScholiaQ127729984MaRDI QIDQ2282599
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
Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Missing data (62D10)
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Cites Work
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- Greedy function approximation: A gradient boosting machine.
- BART: Bayesian additive regression trees
- Incremental tree-based missing data imputation with lexicographic ordering
- Ranking procedures for matched pairs with missing data -- asymptotic theory and a small sample approximation
- Parametric and nonparametric bootstrap methods for general MANOVA
- Improving the precision of classification trees
- Unbiased split selection for classification trees based on the Gini index
- A parametric bootstrap approach for two-way ANOVA in presence of possible interactions with unequal variances
- Multivariate plug-in bandwidth selection
- Accurate mean comparisons for paired samples with missing data: An application to a smoking-cessation trial
- Asymptotics for general multivariate kernel density derivative estimators
- Bootstrap methods for multivariate hypothesis testing
- A parametric bootstrap solution to the MANOVA under heteroscedasticity
- Inference and missing data
- Classification and regression trees and forests for incomplete data from sample surveys
- Permuting incomplete paired data: a novel exact and asymptotic correct randomization test
- Random forests
- Stochastic gradient boosting.