Boosting Prediction with Data Missing Not at Random

Yuan Bian

doi:10.1080/10618600.2025.2541012

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Boosting Prediction with Data Missing Not at Random

2025-10-01

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Yuan Bian

Grace Y. Yi

Wenqing He *

DOI：10.1080/10618600.2025.2541012

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Abstract

En 中文

Boosting has emerged as a useful machine learning technique over the past three decades, attracting increased attention. Most advancements in this area, however, have primarily focused on numerical implementation procedures, often lacking rigorous theoretical justifications. Moreover, these approaches are generally designed for datasets with fully observed data, and their validity can be compromised by the presence of missing observations. In this article, we employ semiparametric estimation approaches to develop boosting prediction methods for data with missing responses. We explore two strategies for adjusting the loss functions to account for missingness effects. The proposed methods are implemented using a functional gradient descent algorithm, and their theoretical properties, including algorithm convergence and estimator consistency, are rigorously established. Numerical studies demonstrate that the proposed methods perform well in finite sample settings. Supplementary materials for this article are available online.

Keywords:

Adjusted loss function

Boosting

Consistency

Missing data

Semiparametric estimation