Variable selection for high-dimensional varying coefficient partially linear models via nonconcave penalty
In this paper, we consider the problem of simultaneous variable selection and estimation for varying-coefficient partially linear models in a “small n , large p ” setting, when the number of coefficients in the linear part diverges with sample size while the number of varying coefficients is fixed....
Main Authors: | Hong, Zhaoping, Hu, Yuao, Lian, Heng |
---|---|
Other Authors: | School of Physical and Mathematical Sciences |
Format: | Journal Article |
Language: | English |
Published: |
2013
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/103043 http://hdl.handle.net/10220/16889 |
Similar Items
-
Variable selection in a partially linear proportional hazards model with a diverging dimensionality
by: Hu, Yuao, et al.
Published: (2013) -
Variable selection for high-dimensional generalized varying-coefficient models
by: Lian, Heng
Published: (2013) -
Time-varying coefficient estimation in differential equation models with noisy time-varying covariates
by: Hong, Zhaoping, et al.
Published: (2013) -
Variable selection in high-dimensional partly linear additive models
by: Lian, Heng
Published: (2013) -
A note on the consistency of Schwarz’s criterion in linear quantile regression with the SCAD penalty
by: Lian, Heng
Published: (2013)