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Parameter Estimation of Alpha Power Inverted Topp-Leone Distribution with Applications
1 High Institute for Management Sciences, Belqas, 35511, Egypt
2 Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, 12613, Egypt
3 Faculty of Business Administration, Delta University for Science and Technology, Mansoura, 11152, Egypt
4 Faculty of Commerce, Mansoura University, Mansoura, 35516, Egypt
* Corresponding Author: Ehab M. Almetwally. Email:
Intelligent Automation & Soft Computing 2021, 29(2), 353-371. https://doi.org/10.32604/iasc.2021.017586
Received 03 February 2021; Accepted 21 March 2021; Issue published 16 June 2021
Abstract
We introduce a new two-parameter lifetime model, referred to alpha power transformed inverted Topp-Leone, derived by combining the alpha power transformation-G family with the inverted Topp-Leone distribution. Structural properties of the proposed distribution are implemented like; quantile function, residual and reversed residual life, Rényi entropy measure, moments and incomplete moments. The maximum likelihood, weighted least squares, maximum product of spacing, and Bayesian methods of estimation are considered. A simulation study is worked out to evaluate the restricted sample properties of the proposed distribution. Numerical results showed that the Bayesian estimates give more accurate results than the corresponding other estimates in the majority of the situations. The flexibility of the suggested model is demonstrated given some applications related to reliability, medicine, and engineering. A real data set is used to illustrate the potentiality of the alpha power transformed inverted Topp-Leone distribution compared to inverted Topp-Leone, inverse Weibull, alpha power inverse Weibull, inverse Lomax, alpha power inverse Lomax, inverse exponential, and alpha power exponential distributions. Criteria measures and their results showed that the suggested distribution is the best candidate for the considered data sets. The alpha power transformed inverted Topp-Leone distribution operates well for lifetime modeling.Keywords
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