On Transmuted Type II Generalized Logistic Distribution with Application

Femi Samuel Adeyinka

doi:doi:10.11648/j.ajam.20190706.15

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On Transmuted Type II Generalized Logistic Distribution with Application

Femi Samuel Adeyinka

Published in American Journal of Applied Mathematics (Volume 7, Issue 6)

Received: 13 November 2019 Accepted: 17 December 2019 Published: 31 December 2019

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Abstract

Introducing extra parameters into the baseline distribution has been a huge breakthrough in research as this enhances more flexibility of the existing models. One of the recent methods is the use of transmutation map which has attracted the interest of many researchers in the last decade. This article investigates the flexibility of transmuted type II generalized logistic distribution. The well-known type II generalized logistic distribution is transmuted using quadratic rank transmutation map to develop a transmuted type II generalized logistic distribution. The map enables the introduction of additional parameter into its parent model to make it more flexible in the analysis of data in various disciplines such as biological sciences, actuarial science, finance and insurance. Some statistical properties of the model are considered and these properties include the moment, quantiles and functions of minimum and maximum order statistics. The estimation issue of the subject model is addressed using method of maximum likelihood estimation. The model is applied to real life data to demonstrate its performance and the comparison of the result of the subject model with its parent model was done using Akaike Information criterion (AIC), Corrected Akaike Information criterion (AICC) and Bayesian Information criterion (BIC) respectively. It is believed that the results from this research work will be of immense contributions in this field and other related disciplines in modelling real data.

Published in	American Journal of Applied Mathematics (Volume 7, Issue 6) This article belongs to the Special Issue On Transmuted Family of Distributions with Applications
DOI	10.11648/j.ajam.20190706.15
Page(s)	177-182
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Generalized Logistic Distribution, Maximum Likelihood, Order Statistics, Parameter Estimation, Transmutatio

References

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[3]	Aryal, G. R, and Tsokos, C. P. (2009). On the transmuted extreme value distribution with application. Nonlinear Analysis: Theory, Methods and Application.71 (12), el401-el407.
[4]	Aryal, G. R, and Tsokos, C. P. (2011). Transmuted Weilbull distribution: A generalization of Weilbull probability distribution. European Journal of Pure and Applied Mathematics. 4 (2), 89-102.
[5]	Aryal, G. R. (2013). Transmuted log-logistic distribution. Journal of Statistics Applications and probability. 2 (1), 11-20.
[6]	Merovci, F., Alizadeh, M., and Hamedani, G. (2016). Another Generalized Transmuted Family of Distributions: Properties and Applications. Austrian Journal of Statistics. 45, 71-93.
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[8]	Merovci, F. (2014). Transmuted Generalized Rayleigh Distribution. Journal of Statistics Applications and Probability. 3 (1), 9-20.
[9]	Merovci, F., Puka, L. (2014). Transmuted Pareto Distribution. Probstat.7, 1-11.
[10]	Merovci, F. (2013). Transmuted Lindley Distribution. International Journal of open Problems in Computer Science and Mathematics. 6 (2), 63-72.
[11]	Adeyinka F. S, and Olapade, A. K. (2019). On Transmuted Four Parameters Generalized Log-Logistic Distribution. International Journal of Statistical Distributions and Applications. 5 (2): 32-37.
[12]	Adeyinka F. S, and Olapade A. K. (2019). A Study on Transmuted Half Logistic Distribution: Properties and Application. International Journal of Statistical Distributions and Applications. 5 (3): 54-59.
[13]	Adeyinka F. S, and Olapade, A. K. (2019). On the Flexibility of a Transmuted Type I Generalized Half-Logistic Distribution with Application. Engineering Mathematics. 3 (1): 13-18.
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Femi Samuel Adeyinka. (2019). On Transmuted Type II Generalized Logistic Distribution with Application. American Journal of Applied Mathematics, 7(6), 177-182. https://doi.org/10.11648/j.ajam.20190706.15

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Femi Samuel Adeyinka. On Transmuted Type II Generalized Logistic Distribution with Application. Am. J. Appl. Math. 2019, 7(6), 177-182. doi: 10.11648/j.ajam.20190706.15

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Femi Samuel Adeyinka. On Transmuted Type II Generalized Logistic Distribution with Application. Am J Appl Math. 2019;7(6):177-182. doi: 10.11648/j.ajam.20190706.15

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@article{10.11648/j.ajam.20190706.15,
  author = {Femi Samuel Adeyinka},
  title = {On Transmuted Type II Generalized Logistic Distribution with Application},
  journal = {American Journal of Applied Mathematics},
  volume = {7},
  number = {6},
  pages = {177-182},
  doi = {10.11648/j.ajam.20190706.15},
  url = {https://doi.org/10.11648/j.ajam.20190706.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20190706.15},
  abstract = {Introducing extra parameters into the baseline distribution has been a huge breakthrough in research as this enhances more flexibility of the existing models. One of the recent methods is the use of transmutation map which has attracted the interest of many researchers in the last decade. This article investigates the flexibility of transmuted type II generalized logistic distribution. The well-known type II generalized logistic distribution is transmuted using quadratic rank transmutation map to develop a transmuted type II generalized logistic distribution. The map enables the introduction of additional parameter into its parent model to make it more flexible in the analysis of data in various disciplines such as biological sciences, actuarial science, finance and insurance. Some statistical properties of the model are considered and these properties include the moment, quantiles and functions of minimum and maximum order statistics. The estimation issue of the subject model is addressed using method of maximum likelihood estimation. The model is applied to real life data to demonstrate its performance and the comparison of the result of the subject model with its parent model was done using Akaike Information criterion (AIC), Corrected Akaike Information criterion (AICC) and Bayesian Information criterion (BIC) respectively. It is believed that the results from this research work will be of immense contributions in this field and other related disciplines in modelling real data.},
 year = {2019}
}

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TY  - JOUR
T1  - On Transmuted Type II Generalized Logistic Distribution with Application
AU  - Femi Samuel Adeyinka
Y1  - 2019/12/31
PY  - 2019
N1  - https://doi.org/10.11648/j.ajam.20190706.15
DO  - 10.11648/j.ajam.20190706.15
T2  - American Journal of Applied Mathematics
JF  - American Journal of Applied Mathematics
JO  - American Journal of Applied Mathematics
SP  - 177
EP  - 182
PB  - Science Publishing Group
SN  - 2330-006X
UR  - https://doi.org/10.11648/j.ajam.20190706.15
AB  - Introducing extra parameters into the baseline distribution has been a huge breakthrough in research as this enhances more flexibility of the existing models. One of the recent methods is the use of transmutation map which has attracted the interest of many researchers in the last decade. This article investigates the flexibility of transmuted type II generalized logistic distribution. The well-known type II generalized logistic distribution is transmuted using quadratic rank transmutation map to develop a transmuted type II generalized logistic distribution. The map enables the introduction of additional parameter into its parent model to make it more flexible in the analysis of data in various disciplines such as biological sciences, actuarial science, finance and insurance. Some statistical properties of the model are considered and these properties include the moment, quantiles and functions of minimum and maximum order statistics. The estimation issue of the subject model is addressed using method of maximum likelihood estimation. The model is applied to real life data to demonstrate its performance and the comparison of the result of the subject model with its parent model was done using Akaike Information criterion (AIC), Corrected Akaike Information criterion (AICC) and Bayesian Information criterion (BIC) respectively. It is believed that the results from this research work will be of immense contributions in this field and other related disciplines in modelling real data.
VL  - 7
IS  - 6
ER  -

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Author Information

Femi Samuel Adeyinka

Department of Mathematics, Obafemi Awolowo University, Ile-Ife, Nigeria

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APA Style

Femi Samuel Adeyinka. (2019). On Transmuted Type II Generalized Logistic Distribution with Application. American Journal of Applied Mathematics, 7(6), 177-182. https://doi.org/10.11648/j.ajam.20190706.15

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ACS Style

Femi Samuel Adeyinka. On Transmuted Type II Generalized Logistic Distribution with Application. Am. J. Appl. Math. 2019, 7(6), 177-182. doi: 10.11648/j.ajam.20190706.15

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AMA Style

Femi Samuel Adeyinka. On Transmuted Type II Generalized Logistic Distribution with Application. Am J Appl Math. 2019;7(6):177-182. doi: 10.11648/j.ajam.20190706.15

Copy | Download

@article{10.11648/j.ajam.20190706.15,
  author = {Femi Samuel Adeyinka},
  title = {On Transmuted Type II Generalized Logistic Distribution with Application},
  journal = {American Journal of Applied Mathematics},
  volume = {7},
  number = {6},
  pages = {177-182},
  doi = {10.11648/j.ajam.20190706.15},
  url = {https://doi.org/10.11648/j.ajam.20190706.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20190706.15},
  abstract = {Introducing extra parameters into the baseline distribution has been a huge breakthrough in research as this enhances more flexibility of the existing models. One of the recent methods is the use of transmutation map which has attracted the interest of many researchers in the last decade. This article investigates the flexibility of transmuted type II generalized logistic distribution. The well-known type II generalized logistic distribution is transmuted using quadratic rank transmutation map to develop a transmuted type II generalized logistic distribution. The map enables the introduction of additional parameter into its parent model to make it more flexible in the analysis of data in various disciplines such as biological sciences, actuarial science, finance and insurance. Some statistical properties of the model are considered and these properties include the moment, quantiles and functions of minimum and maximum order statistics. The estimation issue of the subject model is addressed using method of maximum likelihood estimation. The model is applied to real life data to demonstrate its performance and the comparison of the result of the subject model with its parent model was done using Akaike Information criterion (AIC), Corrected Akaike Information criterion (AICC) and Bayesian Information criterion (BIC) respectively. It is believed that the results from this research work will be of immense contributions in this field and other related disciplines in modelling real data.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - On Transmuted Type II Generalized Logistic Distribution with Application
AU  - Femi Samuel Adeyinka
Y1  - 2019/12/31
PY  - 2019
N1  - https://doi.org/10.11648/j.ajam.20190706.15
DO  - 10.11648/j.ajam.20190706.15
T2  - American Journal of Applied Mathematics
JF  - American Journal of Applied Mathematics
JO  - American Journal of Applied Mathematics
SP  - 177
EP  - 182
PB  - Science Publishing Group
SN  - 2330-006X
UR  - https://doi.org/10.11648/j.ajam.20190706.15
AB  - Introducing extra parameters into the baseline distribution has been a huge breakthrough in research as this enhances more flexibility of the existing models. One of the recent methods is the use of transmutation map which has attracted the interest of many researchers in the last decade. This article investigates the flexibility of transmuted type II generalized logistic distribution. The well-known type II generalized logistic distribution is transmuted using quadratic rank transmutation map to develop a transmuted type II generalized logistic distribution. The map enables the introduction of additional parameter into its parent model to make it more flexible in the analysis of data in various disciplines such as biological sciences, actuarial science, finance and insurance. Some statistical properties of the model are considered and these properties include the moment, quantiles and functions of minimum and maximum order statistics. The estimation issue of the subject model is addressed using method of maximum likelihood estimation. The model is applied to real life data to demonstrate its performance and the comparison of the result of the subject model with its parent model was done using Akaike Information criterion (AIC), Corrected Akaike Information criterion (AICC) and Bayesian Information criterion (BIC) respectively. It is believed that the results from this research work will be of immense contributions in this field and other related disciplines in modelling real data.
VL  - 7
IS  - 6
ER  -

Copy | Download