Conditional screening approaches have emerged as a powerful alternative to the commonly used marginal screening as they can identify marginally weak but conditionally important variables. However, most existing conditional screening methods need to fix the initial conditioning set, which may determine the ultimately selected variables. If the conditioning set is not properly chosen, the methods may produce false negatives and positives. Moreover, screening approaches typically need to involve tuning parameters and extra modeling steps in order to reach a final model. We propose a sequential conditioning approach by dynamically updating the conditioning set with an iterative selection process. We provide its theoretical properties under the framework of generalized linear models. Powered by an extended Bayesian information criterion as the stopping rule, the method will lead to a final model without the need to choose tuning parameters or threshold parameters. The practical utility of the proposed method is examined via extensive simulations and analysis of a real clinical study on predicting multiple myeloma patients' response to treatment based on their genomic profiles. This article is protected by copyright. All rights reserved.

中文翻译：

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构建具有超高维特征的广义线性模型：顺序条件方法


条件筛选方法已成为常用边际筛选的有力替代方案，因为它们可以识别边际较弱但有条件重要的变量。然而，大多数现有的条件筛选方法需要固定初始条件集，这可能决定最终选择的变量。如果没有正确选择条件集，这些方法可能会产生假阴性和假阳性。此外，筛选方法通常需要涉及调整参数和额外的建模步骤才能达到最终模型。我们提出了一种顺序调节方法，通过迭代选择过程动态更新调节集。我们在广义线性模型的框架下提供了它的理论性质。该方法以扩展贝叶斯信息准则作为停止规则为基础，无需选择调整参数或阈值参数即可得出最终模型。通过对真实临床研究的广泛模拟和分析来检验所提出方法的实际效用，该临床研究根据多发性骨髓瘤患者的基因组图谱预测其对治疗的反应。本文受版权保护。版权所有。