Title : ( Exponential convergence rates for the kernel bivariate distribution function estimator under NSD assumption with application to hydrology data )
Authors: azam kheyri , Mohammad Amini , Hadi Jabbari Nooghabi , Abolghasem Bozorgnia , Andri Volodin ,
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Abstract
In this paper, we study the asymptotic behaviour of the kernel bivariate distribution function estimator for negatively superadditive dependent. The exponential convergence rates for the kernel estimator are investigated. Under certain regularity conditions, the optimal bandwidth rate is determined with respect to mean squared error criteria. A simulation study is used to justify the behaviour of the kernel and histogram estimators. As an application a real data set in hydrology is considered and the kernel bivariate distribution function estimator of the data is investigated.
Keywords
, Bivariate distribution function, Kernel estimation, Negative superadditive dependence
@article{paperid:1080691,
author = {Kheyri, Azam and Amini, Mohammad and Jabbari Nooghabi, Hadi and Bozorgnia, Abolghasem and Andri Volodin},
title = {Exponential convergence rates for the kernel bivariate distribution function estimator under NSD assumption with application to hydrology data},
journal = {Communications in Statistics - Theory and Methods},
year = {2020},
volume = {51},
number = {12},
month = {September},
issn = {0361-0926},
pages = {4042--4054},
numpages = {12},
keywords = {Bivariate distribution function; Kernel estimation; Negative superadditive
dependence},
}
%0 Journal Article
%T Exponential convergence rates for the kernel bivariate distribution function estimator under NSD assumption with application to hydrology data
%A Kheyri, Azam
%A Amini, Mohammad
%A Jabbari Nooghabi, Hadi
%A Bozorgnia, Abolghasem
%A Andri Volodin
%J Communications in Statistics - Theory and Methods
%@ 0361-0926
%D 2020
