scholarly journals ROBUST ESTIMATION AND VARIABLE SELECTION FOR PARTIALLY LINEAR ADDITIVE MODEL UNDER MISSING DATA

2018 ◽  
Vol 20 (1) ◽  
pp. 77-99
Author(s):  
Ya-Feng Xia ◽  
◽  
Yu-Mei Wang ◽  
Keyword(s):  
Missing Data ◽  
Variable Selection ◽  
Robust Estimation ◽  
Additive Model ◽  
Linear Additive Model ◽  
Partially Linear ◽  
Selection For ◽  
Partially Linear Additive Model

scholarly journals Automatic Variable Selection for Partially Linear Functional Additive Model and Its Application to the Tecator Data Set

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Yuping Hu ◽  
Sanying Feng ◽  
Liugen Xue
Keyword(s):  
Variable Selection ◽  
Linear Functional ◽  
Additive Model ◽  
Selection Procedure ◽  
Real Data ◽  
Regularity Conditions ◽  
Data Set ◽  
Partially Linear ◽  
Functional Additive ◽  
Selection For

We introduce a new partially linear functional additive model, and we consider the problem of variable selection for this model. Based on the functional principal components method and the centered spline basis function approximation, a new variable selection procedure is proposed by using the smooth-threshold estimating equation (SEE). The proposed procedure automatically eliminates inactive predictors by setting the corresponding parameters to be zero and simultaneously estimates the nonzero regression coefficients by solving the SEE. The approach avoids the convex optimization problem, and it is flexible and easy to implement. We establish the asymptotic properties of the resulting estimators under some regularity conditions. We apply the proposed procedure to analyze a real data set: the Tecator data set.

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