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Journal of Computational and Graphical Statistics Volume 33, 2024 - Issue 2

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Monte Carlo and Optimization Methods

Multiple Imputation Through XGBoost

Yongshi DengDepartment of Statistics, University of Auckland, Auckland, New ZealandCorrespondence[email protected]

https://orcid.org/0000-0001-5845-859X

Thomas LumleyDepartment of Statistics, University of Auckland, Auckland, New Zealand

https://orcid.org/0000-0003-4255-5437

Pages 352-363 | Received 01 Jun 2021, Accepted 30 Jul 2023, Published online: 19 Oct 2023

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https://doi.org/10.1080/10618600.2023.2252501
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Smilja Stokanović, David Đukić & Nadica Miljković. (2024) Robustness of XGBoost Algorithm to Missing Features for Binary Classification of Medical Data. Robustness of XGBoost Algorithm to Missing Features for Binary Classification of Medical Data.

Jiaxi Yang, Yihan Wang, Ye Yang, Kai Ding, Chongning Na & Yao Yang. (2024) Effects of single and multiple imputation strategies on addressing over-fitting issues caused by imbalanced data from various scenarios. Applied Intelligence 54:3, pages 2812-2830.
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Quoc-Thang Phan, Yuan-Kang Wu & Quoc-Dung Phan. (2024) Enhancing One-Day-Ahead Probabilistic Solar Power Forecast With a Hybrid Transformer-LUBE Model and Missing Data Imputation. IEEE Transactions on Industry Applications 60:1, pages 1396-1408.
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Matthias Templ. (2023) Enhancing Precision in Large-Scale Data Analysis: An Innovative Robust Imputation Algorithm for Managing Outliers and Missing Values. Mathematics 11:12, pages 2729.
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