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- Title
Partial correlation screening for varying coeffcient models.
- Authors
Kazemi, Mohammad
- Abstract
In this paper, we propose a two-stage approach for feature se- lection in varying coefficient models with ultra-high-dimensional predictors. Specifically, we first employ partial correlation coefficient for screening, and then penalized rank regression is applied for dimension-reduced varying co- efficient models to further select important predictors and estimate the coefficient functions. Simulation studies are carried out to examine the performance of proposed approach. We also illustrate it by a real data example.
- Subjects
PARTIAL algebras; BIG data; RANDOM noise theory; KERNEL (Mathematics); STATISTICAL correlation
- Publication
Journal of Mathematical Modeling (JMM), 2020, Vol 8, Issue 4, p363
- ISSN
2345-394X
- Publication type
Article
- DOI
10.22124/jmm.2020.15692.1379