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<p>Boosting techniques have gained increasing interest in both machine learning and statistical research. However, many of these methods are primarily designed for complete datasets, which limits their applicability to handle incomplete data such as missing observations. In this paper, we propose the pseudo-outcome strategy to account for missingness effects and describe a functional gradient descent algorithm. Numerical studies demonstrate the satisfactory performance of the proposed method in finite sample settings. Furthermore, we apply the proposed method to analyze the KLIPS Data.</p>

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Empirical investigations of boosting with pseudo-outcome imputation for missing responses

  • 期刊论文

  • 发表日期：

    2025年1月30日

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  • Yuan Bian,
  • Grace Y. Yi,
  • Wenqing He

摘要

Boosting techniques have gained increasing interest in both machine learning and statistical research. However, many of these methods are primarily designed for complete datasets, which limits their applicability to handle incomplete data such as missing observations. In this paper, we propose the pseudo-outcome strategy to account for missingness effects and describe a functional gradient descent algorithm. Numerical studies demonstrate the satisfactory performance of the proposed method in finite sample settings. Furthermore, we apply the proposed method to analyze the KLIPS Data.