Work

Knockoffs Inference for Partially Linear Models with Automatic Structure Discovery

Year: 2025

Type: conference-paper

Abstract: Partially linear models (PLM) have attracted much attention in the field of statistical machine learning. Specially, the ability of variable selection of PLM has been studied extensively due to the high requirement of model interpretability. However, few of the existing works concerns the false discovery rate (FDR) controllability of variable selection associated with PLM. To address this issue, we formulate a new Knockoffs Inference scheme for Linear And Nonlinear Discoverer (called KI-LAND), where FDR is controlled with respect to both linear and nonlinear variables for automatic structure discovery. For the proposed KI-LAND, theoretical guarantees are established for both FDR controllability and power, and experimental evaluations are provided to validate its effectiveness.

Source: Proceedings of the AAAI Conference on Artificial Intelligence

DOI: 10.1609/aaai.v39i20.35404

Authors: Hao Wang, Biqin Song, Hao Deng, Hong Chen

Institution: Huazhong Agricultural University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Natural Science Foundation of Hubei Province, Fundamental Research Funds for the Central Universities

Awards: G2845948881, G4575739800, G6334681769, G6520979317, G8762894098