Name
Title | ||
|---|---|---|
A modified mean-variance feature-screening procedure for ultrahigh-dimensional discriminant analysis |
Abstract | ||
|---|---|---|
Cui et al. (2015) proposed a mean–variance feature-screening method based on the index MV(X|Y). By modifying MV(X|Y) with a weight function, a new index AD(X,Y) is introduced to measure the dependence between X and Y, and a corresponding feature-screening procedure called Anderson–Darling sure independence screening (AD-SIS) is proposed for ultrahigh-dimensional discriminant analysis. The sure screening and ranking consistency properties are established under mild conditions. It is shown that AD-SIS is model free with no specification of model structure and can be applied to multi-classification. Furthermore, AD-SIS is robust against heavy-tailed distributions. As such, it can be used to identify the tail difference for the covariate’s distribution. The finite-sample performance of AD-SIS is assessed by simulation and real data analysis. The results show that, compared with existing methods, AD-SIS can be more competitive for feature screening for ultrahigh-dimensional discriminant analysis. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.csda.2019.02.003 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
Ultrahigh-dimensional data,Feature screening,Sure screening,Model free | Covariate,Weight function,Ranking,Linear discriminant analysis,Statistics,Mathematics | Journal |
Volume | ISSN | Citations |
137 | 0167-9473 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengmei He | 1 | 0 | 0.34 |
| Shuangge Ma | 2 | 440 | 37.37 |
| Wangli Xu | 3 | 9 | 6.40 |