Optimal Quantile Principle for Selecting Variable Bandwidth in Regression Estimators

1992 ◽  
pp. 401-405
Author(s):  
Andrzej S. Kozek ◽  
Eugene F. Schuster
Keyword(s):  
Variable Bandwidth ◽  
Regression Estimators

scholarly journals Variable bandwidth kernel regression estimation

2021 ◽  
Author(s):  
Janet Nakarmi ◽  
Hailin Sang ◽  
Lin Ge
Keyword(s):  
Limit Theorems ◽  
Kernel Method ◽  
Kernel Regression ◽  
Regression Function ◽  
Order Derivative ◽  
Variable Kernel ◽  
Variable Bandwidth ◽  
Regression Estimators ◽  
Kernel Regression Estimation ◽  
Fifth Order

In this paper we propose a variable bandwidth kernel regression estimator for $i.i.d.$ observations in $\mathbb{R}^2$ to improve the classical Nadaraya-Watson estimator. The bias is improved to the order of $O(h_n^4)$ under the condition that the fifth order derivative of the density function and the sixth order derivative of the regression function are bounded and continuous. We also establish the central limit theorems for the proposed ideal and true variable kernel regression estimators. The simulation study confirms our results and demonstrates the advantage of the variable bandwidth kernel method over the classical kernel method.

Robustness of theil's mixed regression estimators

1977 ◽  
Vol 5 (1) ◽  
pp. 93-109 ◽  
Author(s):  
P.A.V.B. Swamy ◽  
J.S. Mehta
Keyword(s):  
Regression Estimators ◽  
Mixed Regression

scholarly journals The influence function of penalized regression estimators

Statistics ◽  
2014 ◽  
Vol 49 (4) ◽  
pp. 741-765 ◽  
Author(s):  
Viktoria Öllerer ◽  
Christophe Croux ◽  
Andreas Alfons
Keyword(s):  
Influence Function ◽  
Penalized Regression ◽  
Regression Estimators

scholarly journals On some beta ridge regression estimators: method, simulation and application

2021 ◽  
pp. 1-14
Author(s):  
Muhammad Qasim ◽  
Kristofer Månsson ◽  
B. M. Golam Kibria
Keyword(s):  
Ridge Regression ◽  
Regression Estimators
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