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The Reflected-Shifted-Truncated Lindley Distribution with Applications

Author

Listed:
  • Dey Sanku

    (Department of Statistics, St. Anthony’s College, Shillong, Meghalaya, India)

  • Waymyers Sophia

    (Department of Mathematics, Francis Marion University, Florence, USA)

  • Kumar Devendra

    (Department of Statistics, Central University of Haryana, Mahendragarh, India)

Abstract

In 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.

Suggested Citation

  • Dey Sanku & Waymyers Sophia & Kumar Devendra, 2020. "The Reflected-Shifted-Truncated Lindley Distribution with Applications," Stochastics and Quality Control, De Gruyter, vol. 35(2), pages 67-77, December.
    • Handle: RePEc:bpj:ecqcon:v:35:y:2020:i:2:p:67-77:n:1
    DOI: 10.1515/eqc-2020-0008
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    References listed on IDEAS

    as
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    3. Kahadawala Cooray & Malwane Ananda, 2010. "Analyzing survival data with highly negatively skewed distribution: The Gompertz-sinh family," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(1), pages 1-11.
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