NDLI: Robust and efficient variable selection for semiparametric partially linear varying coefficient model based on modal regression

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Annals of the Institute of Statistical Mathematics : Volume 69

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Bayesian nonparametric regression with varying residual density

A $$U$$ -statistic approach for a high-dimensional two-sample mean testing problem under non-normality and Behrens–Fisher setting

Local consistency of Markov chain Monte Carlo methods

Bootstrapping continuous-time autoregressive processes

Momentum-space approach to asymptotic expansion for stochastic filtering

Bias-corrected statistical inference for partially linear varying coefficient errors-in-variables models with restricted condition

A truncated estimation method with guaranteed accuracy

Robust and efficient variable selection for semiparametric partially linear varying coefficient model based on modal regression

The harmonic moment tail index estimator: asymptotic distribution and robustness

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Robust and efficient variable selection for semiparametric partially linear varying coefficient model based on modal regression

Content Provider	Springer Nature Link
Author	Zhao, Weihua Zhang, Riquan Liu, Jicai Lv, Yazhao
Copyright Year	2013
Abstract	Semiparametric partially linear varying coefficient models (SPLVCM) are frequently used in statistical modeling. With high-dimensional covariates both in parametric and nonparametric part for SPLVCM, sparse modeling is often considered in practice. In this paper, we propose a new estimation and variable selection procedure based on modal regression, where the nonparametric functions are approximated by $$B$$ -spline basis. The outstanding merit of the proposed variable selection procedure is that it can achieve both robustness and efficiency by introducing an additional tuning parameter (i.e., bandwidth $$h$$ ). Its oracle property is also established for both the parametric and nonparametric part. Moreover, we give the data-driven bandwidth selection method and propose an EM-type algorithm for the proposed method. Monte Carlo simulation study and real data example are conducted to examine the finite sample performance of the proposed method. Both the simulation results and real data analysis confirm that the newly proposed method works very well.
Starting Page	165
Ending Page	191
Page Count	27
File Format	PDF
ISSN	00203157
Journal	Annals of the Institute of Statistical Mathematics
Volume Number	66
Issue Number	1
e-ISSN	15729052
Language	English
Publisher	Springer Japan
Publisher Date	2013-05-29
Publisher Place	Tokyo
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Partially linear varying coefficient model Modal regression $$B$$ -spline Variable selection Oracle property Robustness Efficiency Statistics Statistics for Business/Economics/Mathematical Finance/Insurance
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability

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