A New Family of Generalized Distributions Based on Alpha Power Transformation with Application to Cancer Data

M. Nassar, A. Alzaatreh (), O. Abo-Kasem, M. Mead and M. Mansoor
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M. Nassar: Zagazig University
A. Alzaatreh: American University of Sharjah
O. Abo-Kasem: Zagazig University
M. Mead: Zagazig University
M. Mansoor: The Islamia University of Bahawalpur

Annals of Data Science, 2018, vol. 5, issue 3, No 6, 436 pages

Abstract: Abstract In this paper, we propose a new method for generating distributions based on the idea of alpha power transformation introduced by Mahdavi and Kundu (Commun Stat Theory Methods 46(13):6543–6557, 2017). The new method can be applied to any distribution by inverting its quantile function as a function of alpha power transformation. We apply the proposed method to the Weibull distribution to obtain a three-parameter alpha power within Weibull quantile function. The new distribution possesses a very flexible density and hazard rate function shapes which are very useful in cancer research. The hazard rate function can be increasing, decreasing, bathtub or upside down bathtub shapes. We derive some general properties of the proposed distribution including moments, moment generating function, quantile and Shannon entropy. The maximum likelihood estimation method is used to estimate the parameters. We illustrate the applicability of the proposed distribution to complete and censored cancer data sets.

Keywords: Alpha power transformation; Moments; Weibull distribution; Maximum likelihood estimation; Shannon entropy (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s40745-018-0144-5

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