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The Reflected-Shifted-Truncated Lindley Distribution with Applications

The Reflected-Shifted-Truncated Lindley Distribution with Applications AbstractIn this paper, a new probability density function with bounded domain is presented.The new distribution arises from the Lindley distribution proposed in 1958.It presents the advantage of not including any special function in its formulation.The new transformed model, called the reflected-shifted-truncated Lindley distribution can be used to model left-skewed data.We provide a comprehensive treatment of general mathematical and statistical properties of this distribution.We estimate the model parameters by maximum likelihood methods based on complete and right-censored data.To assess the performance and consistency of the maximum likelihood estimators, we conduct a simulation study with varying sample sizes.Finally, we use the distribution to model left-skewed survival and failure data from two real data sets.For the real data sets containing complete data and right-censored data, this distribution is superior in its ability to sufficiently model the data as compared to the power Lindley, exponentiated power Lindley, generalized inverse Lindley, generalized weighted Lindley and the well-known Gompertz distributions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economic Quality Control de Gruyter

The Reflected-Shifted-Truncated Lindley Distribution with Applications

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Publisher
de Gruyter
Copyright
© 2020 Walter de Gruyter GmbH, Berlin/Boston
ISSN
1869-6147
eISSN
2367-2404
DOI
10.1515/eqc-2020-0008
Publisher site
See Article on Publisher Site

Abstract

AbstractIn this paper, a new probability density function with bounded domain is presented.The new distribution arises from the Lindley distribution proposed in 1958.It presents the advantage of not including any special function in its formulation.The new transformed model, called the reflected-shifted-truncated Lindley distribution can be used to model left-skewed data.We provide a comprehensive treatment of general mathematical and statistical properties of this distribution.We estimate the model parameters by maximum likelihood methods based on complete and right-censored data.To assess the performance and consistency of the maximum likelihood estimators, we conduct a simulation study with varying sample sizes.Finally, we use the distribution to model left-skewed survival and failure data from two real data sets.For the real data sets containing complete data and right-censored data, this distribution is superior in its ability to sufficiently model the data as compared to the power Lindley, exponentiated power Lindley, generalized inverse Lindley, generalized weighted Lindley and the well-known Gompertz distributions.

Journal

Economic Quality Controlde Gruyter

Published: Dec 1, 2020

References