Document Type
Article
Language
eng
Publication Date
2-20-2020
Publisher
Hindawi Publishing Corporation
Source Publication
Computational and Mathematical Methods in Medicine
Source ISSN
1748-670x
Abstract
Statistical distributions play a prominent role in applied sciences, particularly in biomedical sciences. The medical data sets are generally skewed to the right, and skewed distributions can be used quite effectively to model such data sets. In the present study, therefore, we propose a new family of distributions to model right skewed medical data sets. The proposed family may be named as a flexible reduced logarithmic-X family. The proposed family can be obtained via reparameterizing the exponentiated Kumaraswamy G-logarithmic family and the alpha logarithmic family of distributions. A special submodel of the proposed family called, a flexible reduced logarithmic-Weibull distribution, is discussed in detail. Some mathematical properties of the proposed family and certain related characterization results are presented. The maximum likelihood estimators of the model parameters are obtained. A brief Monte Carlo simulation study is done to evaluate the performance of these estimators. Finally, for the illustrative purposes, three applications from biomedical sciences are analyzed and the goodness of fit of the proposed distribution is compared to some well-known competitors.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Liu, Yinglin; Ilyas, Muhammad; Khosa, Saima K.; Mahmoudi, Eisa; Ahmad, Zubair; Khan, Dost Muhammad; and Hamedani, Gholamhossein G., "A Flexible Reduced Logarithmic-X Family of Distributions with Biomedical Analysis" (2020). Mathematical and Statistical Science Faculty Research and Publications. 53.
https://epublications.marquette.edu/math_fac/53
Comments
Published version. Computational and Mathematical Methods in Medicine, Vol. 2020, No. 4373595 (February 20, 2020). DOI. © 2020 Yinglin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Used with permission.