Statistics and Its Interface
Volume 15 (2022)
Partial profile score feature selection in high-dimensional generalized linear interaction models
Pages: 433 – 447
Sequential method is promising for feature selection in high-dimensional models. In this paper, we propose a sequential approach based on partial profile score dubbed as PPSFS to feature selection for a broad class of high-dimensional models, including high-dimensional generalized linear interaction models. The PPSFS approach has a prominent performance in feature selection while it keeps highly scalable for ultra-high-dimensional models. The selection consistency of the PPSFS approach is established under mild conditions. Comprehensive numerical studies demonstrating the performance of PPSFS are reported. A real data analysis for gene expression cancer RNA-Seq data is also presented.
feature selection, partial profile score, high-dimensional, interaction model, sequential procedure
2010 Mathematics Subject Classification
Primary 62F99. Secondary 62J12.
Received 7 June 2021
Accepted 13 October 2021
Published 4 March 2022