EEBoost: A General Method for Prediction and Variable Selection Based on Estimating Equations
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EEBoost：基于估计方程的预测与变量选择
- 研究问题
- 在高维数据存在相关性、缺失或离散程度异常时，如何利用估计方程进行变量选择与预测，避免简单回归遗漏这些特征？
- 方法与思路
- 作者修改标准 boosting 算法，使其适用于估计方程，并分析其与带 L1 约束的投影似然比最小化之间的联系。
- 材料陈述的贡献
- 摘要称，EEBoost 可生成类似惩罚方法的选择路径；在所列模拟中表现较好，计算也快于惩罚估计方程方法。
- 阅读关注点
- 应核对估计方程与更新步骤、和约束优化的联系成立所需条件，以及模拟中相关性、缺失机制和运行时间比较。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要，不能将所列模拟优势推广到所有高维问题；主要证明在补充材料中。
原文摘要
The modern statistical literature is replete with methods for performing variable selection and prediction in standard regression problems. However, simple models may misspecify or fail to capture important aspects of the data generating process such as missingness, correlation, and over/underdispersion. In this article we describe EEBoost, a strategy for variable selection and prediction which can be applied in high-dimensional settings where inference for low-dimensional parameters would typically be based on estimating equations. The method is simple, flexible, and easily implemented using existing software. The EEBoost algorithm is obtained as a modification of the standard boosting (or functional gradient descent) technique. We show that EEBoost is closely related to a class of L1 constrained projected likelihood ratio minimizations, and therefore produces similar variable selection paths to penalized methods without the need to apply constrained optimization algorithms. The flexibility of EEBoost is illustrated by applying it to simulated examples with correlated outcomes and time-to-event data with missing covariates. In both cases, EEBoost outperforms variable selection methods which do not account for the relevant data characteristics. Furthermore, it is shown to be substantially faster to compute than competing methods based on penalized estimating equations. We also apply a version of EEBoost based on the Buckley–James estimating equations to data from an HIV treatment trial, where the aim is to identify mutations which confer resistance to antiretroviral medications. Proofs of the main results appear in the Supplemental Materials (available online).