Volume 19, Issue 2, June 2020, Pages 223 - 237
Marshall–Olkin Power Generalized Weibull Distribution with Applications in Engineering and Medicine
Authors
Ahmed Z. Afify1, Devendra Kumar2, *, I. Elbatal3, 4
1Department of Statistics, Mathematics and Insurance, Benha University, Benha, Egypt
2Department of Statistics, Central University of Haryana, Haryana, India
3Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
4Department of Mathematical Statistics, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt
*Corresponding author. Email: devendrastats@gmail.com
Corresponding Author
Devendra Kumar
Received 12 December 2019, Accepted 26 April 2020, Available Online 21 May 2020.
- DOI
- 10.2991/jsta.d.200507.004How to use a DOI?
- Keywords
- Marshall–Olkin-G Family; Maximum likelihood; Momemts; Power-generalized Weibull model
- Abstract
This paper proposes a new flexible four-parameter model called Marshall–Olkin power generalized Weibull (MOPGW) distribution which provides symmetrical, reversed-J shaped, left-skewed and right-skewed densities, and bathtub, unimodal, increasing, constant, decreasing, J shaped, and reversed-J shaped hazard rates. Some of the MOPGW structural properties are discussed. The maximum likelihood is utilized to estimate the MOPGW unknown parameters. Simulation results are provided to assess the performance of the maximum likelihood method. Finally, we illustrate the importance of the MOPGW model, compared with some rival models, via two real data applications from the engineering and medicine fields.
- Copyright
- © 2020 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Ahmed Z. Afify AU - Devendra Kumar AU - I. Elbatal PY - 2020 DA - 2020/05/21 TI - Marshall–Olkin Power Generalized Weibull Distribution with Applications in Engineering and Medicine JO - Journal of Statistical Theory and Applications SP - 223 EP - 237 VL - 19 IS - 2 SN - 2214-1766 UR - https://doi.org/10.2991/jsta.d.200507.004 DO - 10.2991/jsta.d.200507.004 ID - Afify2020 ER -