Local linear estimation for covariate-adjusted varying-coefficient models

2019 ◽  
Vol 48 (15) ◽  
pp. 3816-3835 ◽  
Author(s):  
Yiqiang Lu ◽  
Feng Li ◽  
Sanying Feng
Keyword(s):  
Varying Coefficient Models ◽  
Linear Estimation ◽  
Varying Coefficient ◽  
Local Linear ◽  
Local Linear Estimation

Functional data: local linear estimation of the conditional density and its application

Statistics ◽  
2013 ◽  
Vol 47 (1) ◽  
pp. 26-44 ◽  
Author(s):  
Jacques Demongeot ◽  
Ali Laksaci ◽  
Fethi Madani ◽  
Mustapha Rachdi
Keyword(s):  
Functional Data ◽  
Conditional Density ◽  
Linear Estimation ◽  
Local Linear ◽  
Local Linear Estimation

Functional data analysis: local linear estimation of the $$L_1$$ L 1 -conditional quantiles

2018 ◽  
Vol 28 (2) ◽  
pp. 217-240 ◽  
Author(s):  
Fahimah A. Al-Awadhi ◽  
Zoulikha Kaid ◽  
Ali Laksaci ◽  
Idir Ouassou ◽  
Mustapha Rachdi
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Linear Estimation ◽  
Conditional Quantiles ◽  
Local Linear ◽  
Local Linear Estimation

scholarly journals Robust estimation and inference for general varying coefficient models with missing observations

Test ◽  
2019 ◽  
Vol 29 (4) ◽  
pp. 966-988
Author(s):  
Francesco Bravo
Keyword(s):  
Nonlinear Least Squares ◽  
Missing At Random ◽  
Varying Coefficient Models ◽  
Finite Sample ◽  
Dimensional Parameter ◽  
Infinite Dimensional ◽  
Varying Coefficient ◽  
Finite Sample Properties ◽  
Local Linear Estimation ◽  
Nonlinear Least Squares Estimation

AbstractThis paper considers estimation and inference for a class of varying coefficient models in which some of the responses and some of the covariates are missing at random and outliers are present. The paper proposes two general estimators—and a computationally attractive and asymptotically equivalent one-step version of them—that combine inverse probability weighting and robust local linear estimation. The paper also considers inference for the unknown infinite-dimensional parameter and proposes two Wald statistics that are shown to have power under a sequence of local Pitman drifts and are consistent as the drifts diverge. The results of the paper are illustrated with three examples: robust local generalized estimating equations, robust local quasi-likelihood and robust local nonlinear least squares estimation. A simulation study shows that the proposed estimators and test statistics have competitive finite sample properties, whereas two empirical examples illustrate the applicability of the proposed estimation and testing methods.

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