A Flexible Reduced Logarithmic-X Family of Distributions with Biomedical Analysis

Author:

Liu Yinglin1,Ilyas Muhammad2,Khosa Saima K.3,Muhmoudi Eisa4ORCID,Ahmad Zubair4ORCID,Khan Dost Muhammad5ORCID,Hamedani G. G6

Affiliation:

1. College of Pharmacy and Chemistry, Dali University, Dali City, Yunnan Province, China

2. Department of Statistics, University of Malakand, Dir (L), Chakdara, Khyber Pakhtunkhwa, Pakistan

3. Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan

4. Department of Statistics, Yazd University, P.O. Box 89175-741, Yazd, Iran

5. Department of Statistics, Abdul Wali University Mardan, Mardan, Khyber Pakhtunkhwa, Pakistan

6. Department of Mathematical and Statistical Sciences, Marquette University, Milwaukee, WI 53201-1881, USA

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.

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine

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