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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Bologna
2018-10-01
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| Series: | Statistica |
| Subjects: | |
| Online Access: | https://rivista-statistica.unibo.it/article/view/8170 |
| Summary: | 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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| ISSN: | 0390-590X 1973-2201 |