Title : ( Convergence Rate for Estimator of Distribution Function under NSD Assumption with an Application )
Authors: azam kheyri , Mohammad Amini , Hadi Jabbari Nooghabi , Abolghasem Bozorgnia ,
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Abstract
In this paper, the kernel distribution function estimator for negative super- additive dependent (NSD) random variables is studied. The exponential inequalities and exponential rate for the kernel estimator are investigated. Under certain regularity conditions, the optimal bandwidth is determined using mean squared error, and is found to be the same as that in the independent identically distributed case. A simula- tion to study the behaviour of the kernel and empirical estimators is given. Moreover, a real data set in hydrology is analyzed to demonstrate the structure of negative super- additive dependence, and as a result, the kernel distribution function estimator of the data is investigated.
Keywords
, Exponential rates, Kernel estimation, Negative superadditive dependence.
@article{paperid:1074549,
author = {Kheyri, Azam and Amini, Mohammad and Jabbari Nooghabi, Hadi and Bozorgnia, Abolghasem},
title = {Convergence Rate for Estimator of Distribution Function under NSD Assumption with an Application},
journal = {Journal of the Iranian Statistical Society},
year = {2019},
volume = {18},
number = {2},
month = {December},
issn = {1726-4057},
pages = {21--37},
numpages = {16},
keywords = {Exponential rates; Kernel estimation; Negative superadditive dependence.},
}
%0 Journal Article
%T Convergence Rate for Estimator of Distribution Function under NSD Assumption with an Application
%A Kheyri, Azam
%A Amini, Mohammad
%A Jabbari Nooghabi, Hadi
%A Bozorgnia, Abolghasem
%J Journal of the Iranian Statistical Society
%@ 1726-4057
%D 2019
