Bahadur Representation of Linear Kernel Quantile Estimator for Stationary Processes

2014 ◽  
Vol 43 (22) ◽  
pp. 4669-4678 ◽  
Author(s):  
Huang Chu ◽  
Zhang Li-Xin
Keyword(s):  
Stationary Processes ◽  
Linear Kernel ◽  
Bahadur Representation ◽  
Kernel Quantile Estimator

scholarly journals Bahadur representation of linear kernel quantile estimator of VaR under assumptions

2010 ◽  
Vol 140 (7) ◽  
pp. 1620-1634 ◽  
Author(s):  
Xianglan Wei ◽  
Shanchao Yang ◽  
Keming Yu ◽  
Xin Yang ◽  
Guodong Xing
Keyword(s):  
Linear Kernel ◽  
Bahadur Representation ◽  
Kernel Quantile Estimator

scholarly journals UNIFORM BAHADUR REPRESENTATION FOR LOCAL POLYNOMIAL ESTIMATES OF M-REGRESSION AND ITS APPLICATION TO THE ADDITIVE MODEL

2010 ◽  
Vol 26 (5) ◽  
pp. 1529-1564 ◽  
Author(s):  
Efang Kong ◽  
Oliver Linton ◽  
Yingcun Xia
Keyword(s):  
Additive Model ◽  
Regression Function ◽  
Stationary Processes ◽  
Local Polynomial Fitting ◽  
Bahadur Representation ◽  
Local Polynomial ◽  
Strongly Mixing ◽  
Higher Order Terms ◽  
Strong Uniform Consistency ◽  
Consistency Rate

We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mixing stationary processes {(Yi,Xi)}. We establish a strong uniform consistency rate for the Bahadur representation of estimators of the regression function and its derivatives. These results are fundamental for statistical inference and for applications that involve plugging such estimators into other functionals where some control over higher order terms is required. We apply our results to the estimation of an additive M-regression model.

Asymptotics for the linear kernel quantile estimator

Test ◽  
2019 ◽  
Vol 28 (4) ◽  
pp. 1144-1174 ◽  
Author(s):  
Xuejun Wang ◽  
Yi Wu ◽  
Wei Yu ◽  
Wenzhi Yang ◽  
Shuhe Hu
Keyword(s):  
Linear Kernel ◽  
Kernel Quantile Estimator

CAPABILITY MEASURES FOR m -DEPENDENT STATIONARY PROCESSES

Statistics ◽  
2003 ◽  
Vol 37 (1) ◽  
pp. 1-24 ◽  
Author(s):  
SY-MIEN CHEN ◽  
YU-SHENG HSU ◽  
W. L. PEARN
Keyword(s):  
Stationary Processes
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