Marshall–Olkin Power Generalized Weibull Distribution with Applications in Engineering and Medicine
- https://doi.org/10.2991/jsta.d.200507.004How to use a DOI?
- Marshall–Olkin-G Family, Maximum likelihood, Momemts, Power-generalized Weibull model
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.
- © 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/).
Cite this article
TY - JOUR AU - Ahmed Z. Afify AU - Devendra Kumar AU - I. Elbatal PY - 2020 DA - 2020/05 TI - Marshall–Olkin Power Generalized Weibull Distribution with Applications in Engineering and Medicine JO - Journal of Statistical Theory and Applications SN - 2214-1766 UR - https://doi.org/10.2991/jsta.d.200507.004 DO - https://doi.org/10.2991/jsta.d.200507.004 ID - Afify2020 ER -