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http://dx.doi.org/10.5351/CSAM.2017.24.3.193

Classical and Bayesian methods of estimation for power Lindley distribution with application to waiting time data  

Sharma, Vikas Kumar (Department of Mathematics, IITRAM)
Singh, Sanjay Kumar (Department of Statistics and DST-CIMS, Banaras Hindu University)
Singh, Umesh (Department of Statistics and DST-CIMS, Banaras Hindu University)
Publication Information
Communications for Statistical Applications and Methods / v.24, no.3, 2017 , pp. 193-209 More about this Journal
Abstract
The power Lindley distribution with some of its properties is considered in this article. Maximum likelihood, least squares, maximum product spacings, and Bayes estimators are proposed to estimate all the unknown parameters of the power Lindley distribution. Lindley's approximation and Markov chain Monte Carlo techniques are utilized for Bayesian calculations since posterior distribution cannot be reduced to standard distribution. The performances of the proposed estimators are compared based on simulated samples. The waiting times of research articles to be accepted in statistical journals are fitted to the power Lindley distribution with other competing distributions. Chi-square statistic, Kolmogorov-Smirnov statistic, Akaike information criterion and Bayesian information criterion are used to access goodness-of-fit. It was found that the power Lindley distribution gives a better fit for the data than other distributions.
Keywords
power Lindley distribution; maximum likelihood estimator; least squares estimator; maximum product spacings estimator; Bayes estimator; goodness-of-fit test;
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