Stable correlation and robust feature screening

Xu Guo*, Runze Li, Wanjun Liu, Lixing Zhu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)


In this paper, we propose a new correlation, called stable correlation, to measure the dependence between two random vectors. The new correlation is well defined without the moment condition and is zero if and only if the two random vectors are independent. We also study its other theoretical properties. Based on the new correlation, we further propose a robust model-free feature screening procedure for ultrahigh dimensional data and establish its sure screening property and rank consistency property without imposing the subexponential or sub-Gaussian tail condition, which is commonly required in the literature of feature screening. We also examine the finite sample performance of the proposed robust feature screening procedure via Monte Carlo simulation studies and illustrate the proposed procedure by a real data example.

Original languageEnglish
Pages (from-to)153-168
Number of pages16
JournalScience China Mathematics
Issue number1
Early online date30 Apr 2021
Publication statusPublished - Jan 2022

Scopus Subject Areas

  • Mathematics(all)

User-Defined Keywords

  • 62H12
  • 62H20
  • feature screening
  • nonlinear dependence
  • stable correlation
  • sure screening property


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