American Journal of Applied Mathematics

Special Issue

On Transmuted Family of Distributions with Applications

  • Submission Deadline: May 20, 2020
  • Status: Submission Closed
  • Lead Guest Editor: Femi Samuel Adeyinka
About This Special Issue
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
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

Published Articles
  • On Transmuted Type II Generalized Logistic Distribution with Application

    Femi Samuel Adeyinka

    Issue: Volume 7, Issue 6, December 2019
    Pages: 177-182
    Received: Nov. 13, 2019
    Accepted: Dec. 17, 2019
    Published: Dec. 31, 2019
    DOI: 10.11648/j.ajam.20190706.15
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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... Show More