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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Format: | Article |
Language: | English |
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University of Bologna
2018-10-01
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Series: | Statistica |
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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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format | Article |
id | doaj.art-8f9a9d308ced4d138be8d7acd5c92c56 |
institution | Directory Open Access Journal |
issn | 0390-590X 1973-2201 |
language | English |
last_indexed | 2024-04-14T08:16:49Z |
publishDate | 2018-10-01 |
publisher | University of Bologna |
record_format | Article |
series | Statistica |
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 |