Variable Selection in Nonparametric and Semiparametric Regression Models

Variable Selection in Nonparametric and Semiparametric Regression Models

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 c...

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Bibliographic Details
Main Authors: SU, Liangjun, ZHANG, Yonghui
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2013
Subjects:
Cross validation
High dimensionality
Lasso
Nonparametric regression
Oracle property
Penalized least squares
Penalized likelihood
SCAD
Semiparametric regression
Sparsity
Variable selection
Econometrics
Statistics and Probability
Online Access:https://ink.library.smu.edu.sg/soe_research/1497
https://ink.library.smu.edu.sg/context/soe_research/article/2496/viewcontent/variable_20selection_20in_20np_20and_20sp20120918.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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