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2023 | 33 | 4 |

Article title

Analysis of COVID-19 and cancer data using new half-logistic generated family of distributions

Content

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Abstracts

EN
We focus on a specific sub-model of the proposed family that we call the new half logistic-Fréchet. This sub-model stems from a new generalisation of the half-logistic distribution which we call the new half-logistic-G. The novelty of proposing this new family is that it does not include any additional parameters and instead relies on the baseline parameter. Standard statistical formulas are used to show the forms of the density and failure rate functions, ordinary and incomplete moments with generating functions, and random variate generation. The maximum likelihood estimation procedure is used to estimate the set of parameters. We conduct a simulation analysis to ensure that our calculations are converging with lower mean square error and biases. We use three real-life data sets to equate our model to well-established existing models. The proposed model outperforms the well-established four parameters beta Fréchet and exponentiated generalized Fréchet for some real- -life results, with three parameters such as half-logistic Fréchet, exponentiated Fréchet, Zografos–Balakrishnan gamma Fréchet, Topp–Leonne Fréchet, and Marshall–Olkin Fréchet and two-parameter classical Fréchet distribution.

Year

Volume

33

Issue

4

Physical description

Dates

published
2023

Contributors

author
  • Department of Statistics, The Islamia University of Bahawalpur, Punjab, Pakistan
  • Department of Social and Allied Sciences, Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Punjab, Pakistan
  • Department of Statistics, The Islamia University of Bahawalpur, Punjab, Pakistan
author
  • Department of Statistics, The Islamia University of Bahawalpur, Punjab, Pakistan

References

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  • [2] Al-Marzouki, S., Jamal, F., Chesneau, C., and Elgarhy, M. Topp-Leone odd Fréchet generated family of distributions with applications to COVID-19 data sets. Computer Modeling in Engineering & Sciences 125, 1 (2020), 437–458.
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  • [9] Granzotto, D. C. T., Louzada, F., and Balakrishnan, N. Cubic rank transmuted distributions: inferential issues and applications. Simulation 87, 14 (2017), 2760–2778.
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Document Type

Publication order reference

Identifiers

Biblioteka Nauki
29127967

YADDA identifier

bwmeta1.element.ojs-doi-10_37190_ord230405
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