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α-Power transformed transformed power function distribution with applications

By standard transformation of a random variable, we obtained a partially bounded one-parameter version of the bounded three-parameter power function distribution by Saran and Pandey (2004) which we called the Transformed Power Function (TPF) distribution and based on an alpha-power transformation me...

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Autores principales: Okorie, Idika E., Ohakwe, Johnson, Osu, Bright O., Onyemachi, Chris U.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481980/
https://www.ncbi.nlm.nih.gov/pubmed/34622056
http://dx.doi.org/10.1016/j.heliyon.2021.e08047
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author Okorie, Idika E.
Ohakwe, Johnson
Osu, Bright O.
Onyemachi, Chris U.
author_facet Okorie, Idika E.
Ohakwe, Johnson
Osu, Bright O.
Onyemachi, Chris U.
author_sort Okorie, Idika E.
collection PubMed
description By standard transformation of a random variable, we obtained a partially bounded one-parameter version of the bounded three-parameter power function distribution by Saran and Pandey (2004) which we called the Transformed Power Function (TPF) distribution and based on an alpha-power transformation method due to Mahdavi and Kundu (2017) we generalized the TPF distribution as the α-Power Transformed Transformed Power Function (αPTTPF) distribution. Some of the properties of the αPTTPF distribution are given, and we approached the parameter estimation by three methods, namely: maximum likelihood, ordinary least-squares, and weighted least-squares, but after comparing the results from a simulation study, we settled for the maximum likelihood. The new distribution is suitable for modeling data with either decreasing or upside-down bathtub hazard rates. Three real data-sets are used to demonstrate the usefulness of the new model.
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spelling pubmed-84819802021-10-06 α-Power transformed transformed power function distribution with applications Okorie, Idika E. Ohakwe, Johnson Osu, Bright O. Onyemachi, Chris U. Heliyon Research Article By standard transformation of a random variable, we obtained a partially bounded one-parameter version of the bounded three-parameter power function distribution by Saran and Pandey (2004) which we called the Transformed Power Function (TPF) distribution and based on an alpha-power transformation method due to Mahdavi and Kundu (2017) we generalized the TPF distribution as the α-Power Transformed Transformed Power Function (αPTTPF) distribution. Some of the properties of the αPTTPF distribution are given, and we approached the parameter estimation by three methods, namely: maximum likelihood, ordinary least-squares, and weighted least-squares, but after comparing the results from a simulation study, we settled for the maximum likelihood. The new distribution is suitable for modeling data with either decreasing or upside-down bathtub hazard rates. Three real data-sets are used to demonstrate the usefulness of the new model. Elsevier 2021-09-23 /pmc/articles/PMC8481980/ /pubmed/34622056 http://dx.doi.org/10.1016/j.heliyon.2021.e08047 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Okorie, Idika E.
Ohakwe, Johnson
Osu, Bright O.
Onyemachi, Chris U.
α-Power transformed transformed power function distribution with applications
title α-Power transformed transformed power function distribution with applications
title_full α-Power transformed transformed power function distribution with applications
title_fullStr α-Power transformed transformed power function distribution with applications
title_full_unstemmed α-Power transformed transformed power function distribution with applications
title_short α-Power transformed transformed power function distribution with applications
title_sort α-power transformed transformed power function distribution with applications
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481980/
https://www.ncbi.nlm.nih.gov/pubmed/34622056
http://dx.doi.org/10.1016/j.heliyon.2021.e08047
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