Statistics and Computing

, Volume 27, Issue 2, pp 535–545 | Cite as

Robust rank screening for ultrahigh dimensional discriminant analysis

  • Guosheng Cheng
  • Xingxiang Li
  • Peng LaiEmail author
  • Fengli Song
  • Jun Yu


In this paper, we consider sure independence feature screening for ultrahigh dimensional discriminant analysis. We propose a new method named robust rank screening based on the conditional expectation of the rank of predictor’s samples. We also establish the sure screening property for the proposed procedure under simple assumptions. The new procedure has some additional desirable characters. First, it is robust against heavy-tailed distributions, potential outliers and the sample shortage for some categories. Second, it is model-free without any specification of a regression model and directly applicable to the situation with many categories. Third, it is simple in theoretical derivation due to the boundedness of the resulting statistics. Forth, it is relatively inexpensive in computational cost because of the simple structure of the screening index. Monte Carlo simulations and real data examples are used to demonstrate the finite sample performance.


Feature screening Robust property of rank Sure screening property Ultrahigh dimensional discriminant analysis 



The authors thank the editor and two referees for their valuable comments and suggestions. Peng Lai’s research was supported by National Natural Science Foundation of China (Grant No. 11301279). Fengli Song’s research was supported by Natural Science Foundation of Jiangsu Province for Youth (Grant No. BK20140983).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Guosheng Cheng
    • 1
  • Xingxiang Li
    • 1
  • Peng Lai
    • 1
    Email author
  • Fengli Song
    • 1
  • Jun Yu
    • 2
  1. 1.School of Mathematics and StatisticsNanjing University of Information Science & TechnologyNanjingChina
  2. 2.Department of Mathematics and StatisticsUniversity of VermontBurlingtonUSA

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