Publication Type
Book Chapter
Version
submittedVersion
Publication Date
1-2013
Abstract
This chapter reviews the literature on variable selection in nonparametric and semiparametric regression models via shrinkage. We highlight recent developments on simultaneous variable selection and estimation through the methods of least absolute shrinkage and selection operator (Lasso), smoothly clipped absolute deviation (SCAD) or their variants, but restrict our attention to nonparametric and semiparametric regression models. In particular, we consider variable selection in additive models, partially linear models, functional/varying coefficient models, single index models, general nonparametric regression models, and semiparametric/nonparametric quantile regression models.
Keywords
Cross validation, High dimensionality, Lasso, Nonparametric regression, Oracle property, Penalized least squares, Penalized likelihood, SCAD, Semiparametric regression, Sparsity, Variable selection
Discipline
Econometrics | Statistics and Probability
Research Areas
Econometrics
Publication
Handbook in Applied Nonparametric and Semi-Nonparametric Econometrics and Statistics
Editor
Racine, Jeffrey S.; SU, Liangjun; Ullah, Aman
ISBN
9780199857944
Identifier
10.1093/oxfordhb/9780199857944.013.009
Publisher
Oxford University Press
City or Country
Oxford
Citation
SU, Liangjun and ZHANG, Yonghui.
Variable Selection in Nonparametric and Semiparametric Regression Models. (2013). Handbook in Applied Nonparametric and Semi-Nonparametric Econometrics and Statistics.
Available at: https://ink.library.smu.edu.sg/soe_research/1497
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1093/oxfordhb/9780199857944.013.009
Comments
forthcoming