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The exponentiated Perks distribution

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Abstract

The study proposes the exponentiated Perks distribution as a generalization of Perks distribution. This generalized distribution provides monotone nondecreasing and bathtub shaped hazard rate function. We study its mathematical properties including mode, median, quantile function and order statistics. The estimation of the model parameters is discussed both in classical and Bayesian setups. The maximum likelihood estimates along with their standard errors and confidence intervals have been obtained. For Bayesian estimation, we use independent gamma priors for the model parameters. The posterior densities of the parameters are simulated using Metropolis–Hastings algorithm to obtain sample-based estimates and highest posterior density intervals. Applications of the proposed distribution to three real data sets have been demonstrated.

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Acknowledgments

The authors thankfully acknowledge the critical suggestions and comments from the learned referee which helped us in the improvement of the paper.

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Authors and Affiliations

  1. Department of Statistics, Ch. Charan Singh University, Meerut, India

    Bhupendra Singh & Neha Choudhary

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  1. Bhupendra Singh
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  2. Neha Choudhary
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Correspondence to Bhupendra Singh.

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Singh, B., Choudhary, N. The exponentiated Perks distribution. Int J Syst Assur Eng Manag 8, 468–478 (2017). https://doi.org/10.1007/s13198-016-0451-1

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  • DOI: https://doi.org/10.1007/s13198-016-0451-1

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