Publicação

Sure Screening with Kernel-Based Distance Correlation: Methodology and Applications: Accepted December 2025

Detalhes bibliográficos
Resumo:	We consider a generalized kernel-based distance correlation measure for feature screening in high-dimensional settings. Theoretical results establish the sure screening property under mild regularity conditions for a class of negative-definite kernels. The method is flexible, requiring minimal distributional assumptions, and can be naturally extended to multivariate responses and grouped features. Extensive simulation studies confirm its robustness and effectiveness, while applications to real-world biomedical datasets demonstrate its practical relevance. The results highlight the potential of kernel-based distance measures as a powerful and scalable tool for variable selection in complex data environments.
Autores principais:	Milošević, Bojana
Outros Autores:	Radojević, Jelena; Milošević, Bojana
Assunto:	distance correlation circular data hyperspherical data sure screening model-free selection
Ano:	2025
País:	Portugal
Tipo de documento:	artigo
Tipo de acesso:	unknown
Instituição associada:	Instituto Nacional de Estatística
Idioma:	inglês
Origem:	REVSTAT-Statistical Journal

Descrição
Resumo:	We consider a generalized kernel-based distance correlation measure for feature screening in high-dimensional settings. Theoretical results establish the sure screening property under mild regularity conditions for a class of negative-definite kernels. The method is flexible, requiring minimal distributional assumptions, and can be naturally extended to multivariate responses and grouped features. Extensive simulation studies confirm its robustness and effectiveness, while applications to real-world biomedical datasets demonstrate its practical relevance. The results highlight the potential of kernel-based distance measures as a powerful and scalable tool for variable selection in complex data environments.