Publication Type

Book Chapter

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, Oxford

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1093/oxfordhb/9780199857944.013.009

Comments

forthcoming

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