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On Transmuted Family of Distributions with Applications
Submission Deadline: Feb. 20, 2020

This special issue currently is open for paper submission and guest editor application.

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Lead Guest Editor
Femi Samuel Adeyinka
Mathematics, Science, Obafemi Awolowo University, Ile-Ife, Nigeria
Guest Editors
  • Akintayo Kehinde Olapade
    Mathematics, Science, Obafemi Awolowo University, Ile-Ife, Nigeria
  • Sule Ibrahim
    Mathematics, Science, Ahmadu Bello University, Zaria, Nigeria
  • Oladipupo Ibukun Ojemola
    Mathematics and Statistics, Science, Bowen University, Iwo, Nigeria
  • Abiodun Oyekunle
    Mathematics, Science, Obafemi Awolowo University, Ile-Ife, Nigeria
  • Oluokun Kasali Agunloye
    Mathematics, Science, Obafemi Awolowo University, Ile-Ife, Nigeria
  • Olalekan Akanji Bello
    Mathematics, Science, Ahmadu Bello University, Zaria, Nigeria
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
Papers should be formatted according to the guidelines for authors (see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=148). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
Published Papers
The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.

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Special Issue

Introduction
Many probability models have been developed over the years to model data from various disciplines of human endeavours. There still remain many areas where these existing models cannot be used to model data that arise from these fields. As a result there is a clear need for improvement on the existing ones to ensure they are more flexible in handling various relevant data. This special issue will look into the transmutation of some well-established probability models, establish their mathematical properties such as mean, median, mode,variance,moments, quantiles ,characteristics function and moment generating function.Their order statistics will be given proper consideration ranging from minimum to maximum order statistics. The estimation issues will be addressed using any appropriate estimation method such as maximum likelihood estimation method (MLE).Various areas of applications will be looked into such as reliability analysis, survival analysis, finance, medicine, economics, actuarial science and insurance to demonstrate their applicability and flexibility in statistical analysis of data arising from these fields. Their performances will be tested using the appropriate statistical tests such as Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICC) and Bayesian Information Criterion (BIC). These new models will be characterized by relating them to the existing ones in the literature and some theorems will be stated and proved where necessary to establish these relationships. We hope this special issue will provide helps to researchers in this field and other related disciplines as this will birth some new probability models whose properties will be established.
Aims and Scope:
  1. To obtain the transmuted versions of some existing probability models to enhance more flexibility in data analysis
  2. To establish their mathematical properties such as mean, median, mode,variance,moments, quantiles etc
  3. To study the order statistics from the new models and relate them to the one from their parent models
  4. To also address the estimation issues using any appropriate estimation method
  5. To demonstrate the applicability of these new models over their baseline models
  6. To also compare their goodness of fit to their parent models using the appropriate statistical tests
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