Alexadria Engineering Journal, Volume (59), No (4), Year (2020-8) , Pages (2323-2330)

Title : ( Density derivative estimation for stationary and strongly mixing data )

Authors: M. Mahmoudi , A Nezakati , Mohammad Arashi , M R Mahmoudi ,

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Abstract

Estimation of density derivatives has found multiple uses in statistical data analysis. An inefficient two-step method to obtain it is estimating the density and then computing the derivatives. This method does not render good results since a good density estimator is not always a suitable density-derivative estimator. The present paper studies the kernel type as a non-parametric estimation of the density function derivative connected with a highly mixing time series. To improve estimation accuracy in asymptotic mean squared error sense, a shrinkage type estimator is defined, under the prior information that the derivative is known. In addition to the investigation of asymptotic distributional properties, a simulation study is carried out to numerically demonstrate the findings. Effect of deviating from the prior information on estimation is also considered and discussed.

Keywords

, Density derivatives, Improved estimator, Kernel density estimation, alpha-mixing processes, Shrinkage, Positive-rule Stein-type estimator
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@article{paperid:1081330,
author = {M. Mahmoudi and A Nezakati and Arashi, Mohammad and M R Mahmoudi},
title = {Density derivative estimation for stationary and strongly mixing data},
journal = {Alexadria Engineering Journal},
year = {2020},
volume = {59},
number = {4},
month = {August},
issn = {1110-0168},
pages = {2323--2330},
numpages = {7},
keywords = {Density derivatives; Improved estimator; Kernel density estimation; alpha-mixing processes; Shrinkage; Positive-rule Stein-type estimator},
}

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%0 Journal Article
%T Density derivative estimation for stationary and strongly mixing data
%A M. Mahmoudi
%A A Nezakati
%A Arashi, Mohammad
%A M R Mahmoudi
%J Alexadria Engineering Journal
%@ 1110-0168
%D 2020

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