Variable selection in high-dimensional partly linear additive models
Semiparametric models are particularly useful for high-dimensional regression problems. In this paper, we focus on partly linear additive models with a large number of predictors (can be larger than the sample size) and consider model estimation and variable selection based on polynomial spline expa...
Saved in:
Main Author: | Lian, Heng |
---|---|
Other Authors: | School of Physical and Mathematical Sciences |
Format: | Article |
Language: | English |
Published: |
2013
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/97695 http://hdl.handle.net/10220/17096 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Estimation by polynomial splines with variable selection in additive Cox models
by: Zhang, Shangli, et al.
Published: (2013) -
SCAD-penalised generalised additive models with non-polynomial dimensionality
by: Li, Gaorong, et al.
Published: (2013) -
Generalized additive partial linear models with high-dimensional covariates
by: Lian, Heng, et al.
Published: (2014) -
Variable selection for high-dimensional varying coefficient partially linear models via nonconcave penalty
by: Hong, Zhaoping, et al.
Published: (2013) -
Generalized additive partial linear models for clustered data with diverging number of covariates using gee
by: Wang, Lan, et al.
Published: (2014)