Estimation and variable selection for partially linear additive models with measurement errors

2019 ◽  
pp. 1-30
Author(s):  
Rui Li ◽  
Shuchuan Mu ◽  
Ruili Hao
Keyword(s):  
Variable Selection ◽  
Measurement Errors ◽  
Additive Models ◽  
Partially Linear ◽  
Selection For

scholarly journals Variable selection for partially linear models via learning gradients

2017 ◽  
Vol 11 (2) ◽  
pp. 2907-2930 ◽  
Author(s):  
Lei Yang ◽  
Yixin Fang ◽  
Junhui Wang ◽  
Yongzhao Shao
Keyword(s):  
Variable Selection ◽  
Linear Models ◽  
Partially Linear Models ◽  
Partially Linear ◽  
Selection For

Variable selection for ultra-high dimensional quantile regression with missing data and measurement error

2020 ◽  
pp. 096228022094153
Author(s):  
Yongxin Bai ◽  
Maozai Tian ◽  
Man-Lai Tang ◽  
Wing-Yan Lee
Keyword(s):  
Missing Data ◽  
Measurement Error ◽  
Variable Selection ◽  
Quantile Regression ◽  
Measurement Errors ◽  
Estimation Method ◽  
High Dimensional ◽  
Data Set ◽  
Cancer Data ◽  
Selection For

In this paper, we consider variable selection for ultra-high dimensional quantile regression model with missing data and measurement errors in covariates. Specifically, we correct the bias in the loss function caused by measurement error by applying the orthogonal quantile regression approach and remove the bias caused by missing data using the inverse probability weighting. A nonconvex Atan penalized estimation method is proposed for simultaneous variable selection and estimation. With the proper choice of the regularization parameter and under some relaxed conditions, we show that the proposed estimate enjoys the oracle properties. The choice of smoothing parameters is also discussed. The performance of the proposed variable selection procedure is assessed by Monte Carlo simulation studies. We further demonstrate the proposed procedure with a breast cancer data set.

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