Title : ( Kernel density estimation under negative superadditive dependence and its application for real data )
Authors: azam kheyri , Mohammad Amini , Hadi Jabbari Nooghabi , Abolghasem Bozorgnia ,
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Abstract
In this paper, the kernel density estimator for negatively superadditive dependent random variables is studied. The exponential inequalities and the exponential rate for the kernel estimator of density function with a uniform version, over compact sets are investigated. Also, the optimal bandwidth rate of the estimator is obtained using mean integrated squared error. The results are generalized and used to improve the ones obtained for the case of associated sequences. As an application, FGM sequences that fulfil our assumptions are investigated. Also, the convergence rate of the kernel density estimator is illustrated via a simulation study. Moreover, a real data analysis is presented.
Keywords
Exponential rates; kernel estimation; negative superadditive dependence
@article{paperid:1074488,
author = {Kheyri, Azam and Amini, Mohammad and Jabbari Nooghabi, Hadi and Bozorgnia, Abolghasem},
title = {Kernel density estimation under negative superadditive dependence and its application for real data},
journal = {Journal of Statistical Computation and Simulation},
year = {2019},
volume = {89},
number = {12},
month = {May},
issn = {0094-9655},
pages = {2373--2392},
numpages = {19},
keywords = {Exponential rates; kernel estimation; negative superadditive dependence},
}
%0 Journal Article
%T Kernel density estimation under negative superadditive dependence and its application for real data
%A Kheyri, Azam
%A Amini, Mohammad
%A Jabbari Nooghabi, Hadi
%A Bozorgnia, Abolghasem
%J Journal of Statistical Computation and Simulation
%@ 0094-9655
%D 2019
