International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 9, Issue 11 (November 2022), Pages: 51-63

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 Original Research Paper

 The effect of exponentiating generalized models

 Author(s): Hadeel S. Klakattawi *, Aisha A. Khormi

 Affiliation(s):

 Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-6617-2081

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.11.006

 Abstract:

A common practice in statistical distribution theory involves exponentiating existing distribution functions to include some extra parameters that increase the flexibility of the distribution. This paper examines the effect of exponentiating some generalized models by adding three extra parameters to their probability distribution. Particularly, a new generalized distribution that is a member of the inverted Kumaraswamy family of distributions is considered. Afterward, three additional parameters are applied to enhance this generalized distribution, which results in a novel distribution referred to as the new generalized exponentiated generalized inverted Kumaraswamy Gompertz distribution (NGEGIKGD). Some of the statistical and mathematical characteristics of this distribution were derived. Additionally, parametric estimation of the new distribution parameters was considered using the maximum likelihood method. Several Monte Carlo simulation studies were conducted in order to explore the usefulness of the estimation method. The proposed distribution is then compared with its corresponding sub-models in order to assess the effects of the exponentiation. Further evaluation of the distribution is accomplished by comparing it to some relative distributions. Specifically, three real-world datasets were analyzed to demonstrate the potentiality of the suggested new modeling approach in enhancing the goodness of fit of the generalized models. Results indicate that exponentiating a generalized model significantly improves its fit compared to the non-exponentiating distributions.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: T-X family, Exponentiated, Generalized Inverted Kumaraswamy distribution, Gompertz distribution, Maximum likelihood, Monte Carlo simulation

 Article History: Received 1 April 2022, Received in revised form 12 July 2022, Accepted 28 July 2022

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Klakattawi HS and Khormi AA  (2022). The effect of exponentiating generalized models. International Journal of Advanced and Applied Sciences, 9(11): 51-63

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 Table 11 Table 12 Table 13 

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