Methods of Estimating the Parameters of the Quasi Lindley Distribution

In this paper, we review the quasi Lindley distribution and established its quantile function. A simulation study is conducted to examine the bias and mean square error of the parameter estimates of the distribution through the method of moment estimation and the maximum likelihood estimation. Resul...

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Main Authors: Festus Opone, Nosakhare Ekhosuehi
Format: Article
Language:English
Published: University of Bologna 2018-10-01
Series:Statistica
Subjects:
Online Access:https://rivista-statistica.unibo.it/article/view/8170
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author Festus Opone
Nosakhare Ekhosuehi
author_facet Festus Opone
Nosakhare Ekhosuehi
author_sort Festus Opone
collection DOAJ
description In this paper, we review the quasi Lindley distribution and established its quantile function. A simulation study is conducted to examine the bias and mean square error of the parameter estimates of the distribution through the method of moment estimation and the maximum likelihood estimation. Result obtained shows that the method of maximum likelihood is a better choice of estimation method for the parameters of the quasi Lindley distribution. Finally, an applicability of the quasi Lindley disttribution to a waiting time data set suggests that the distribution demonstrates superiority over the power Lindley distribution, Sushila distribution and the classical oneparameter Lindley distribution in terms of the maximized loglikelihood, the Akaike information criterion, the Kolmogorov-Smirnov and Cramér von Mises test statistic.
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spelling doaj.art-8f9a9d308ced4d138be8d7acd5c92c562022-12-22T02:04:23ZengUniversity of BolognaStatistica0390-590X1973-22012018-10-0178218319310.6092/issn.1973-2201/81707484Methods of Estimating the Parameters of the Quasi Lindley DistributionFestus Opone0Nosakhare Ekhosuehi1University of BeninUniversity of BeninIn this paper, we review the quasi Lindley distribution and established its quantile function. A simulation study is conducted to examine the bias and mean square error of the parameter estimates of the distribution through the method of moment estimation and the maximum likelihood estimation. Result obtained shows that the method of maximum likelihood is a better choice of estimation method for the parameters of the quasi Lindley distribution. Finally, an applicability of the quasi Lindley disttribution to a waiting time data set suggests that the distribution demonstrates superiority over the power Lindley distribution, Sushila distribution and the classical oneparameter Lindley distribution in terms of the maximized loglikelihood, the Akaike information criterion, the Kolmogorov-Smirnov and Cramér von Mises test statistic.https://rivista-statistica.unibo.it/article/view/8170Quasi Lindley distributionQuantile functionMoment estimationMaximum likelihood estimation
spellingShingle Festus Opone
Nosakhare Ekhosuehi
Methods of Estimating the Parameters of the Quasi Lindley Distribution
Statistica
Quasi Lindley distribution
Quantile function
Moment estimation
Maximum likelihood estimation
title Methods of Estimating the Parameters of the Quasi Lindley Distribution
title_full Methods of Estimating the Parameters of the Quasi Lindley Distribution
title_fullStr Methods of Estimating the Parameters of the Quasi Lindley Distribution
title_full_unstemmed Methods of Estimating the Parameters of the Quasi Lindley Distribution
title_short Methods of Estimating the Parameters of the Quasi Lindley Distribution
title_sort methods of estimating the parameters of the quasi lindley distribution
topic Quasi Lindley distribution
Quantile function
Moment estimation
Maximum likelihood estimation
url https://rivista-statistica.unibo.it/article/view/8170
work_keys_str_mv AT festusopone methodsofestimatingtheparametersofthequasilindleydistribution
AT nosakhareekhosuehi methodsofestimatingtheparametersofthequasilindleydistribution