Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approach.

ABSTRACT: 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 b

SUBMITTER: Zheng Q 

PROVIDER: S-EPMC7136011 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature