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Dynamic tilted current correlation for high dimensional variable screening
- Source :
- Journal of Multivariate Analysis. 182:104693
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Variable screening is a commonly used procedure in high dimensional data analysis to reduce dimensionality and ensure the applicability of available statistical methods. Such a procedure is complicated and computationally burdensome because spurious correlations commonly exist among predictor variables, while important predictor variables may not have large marginal correlations with the response variable. To circumvent these issues, in this paper, we develop a new screening technique, the “dynamic tilted current correlation screening” (DTCCS), for high dimensional variable screening. DTCCS is capable of selecting the most relevant predictors within a finite number of steps, and takes the popularly used sure independence screening (SIS) method and the high-dimensional ordinary least squares projection (HOLP) approach as its special cases. The DTCCS technique has sure screening and consistency properties which are justified theoretically and demonstrated numerically. A real example of gene expression data is analyzed using the proposed DTCCS procedure.
- Subjects :
- Statistics and Probability
Clustering high-dimensional data
Numerical Analysis
020206 networking & telecommunications
02 engineering and technology
01 natural sciences
010104 statistics & probability
Variable (computer science)
Consistency (statistics)
Ordinary least squares
0202 electrical engineering, electronic engineering, information engineering
0101 mathematics
Statistics, Probability and Uncertainty
Projection (set theory)
Spurious relationship
Algorithm
Independence (probability theory)
Curse of dimensionality
Mathematics
Subjects
Details
- ISSN :
- 0047259X
- Volume :
- 182
- Database :
- OpenAIRE
- Journal :
- Journal of Multivariate Analysis
- Accession number :
- edsair.doi...........5c2ea0de4e9bc34bbf33236a89bdb57c