The Burr-Weibull Power Series Class of Distributions

  • Broderick Oluyede Georgia Southern University
  • Precious Mdlongwa
  • Boikanyo Makubate Botswana International University of Science and Technology
  • Shujiao Huang University of Houston


A new generalized class of distributions called the Burr-Weibull Power Series (BWPS) class of distributions is developed and explored. This class of distributions generalizes the Burr power series and Weibull power series classes of distributions, respectively. A special model of the BWPS class of distributions, the new Burr-Weibull Poisson (BWP) distribution is considered and some of its mathematical properties are obtained. The BWP distribution contains several new and well known sub-models, including Burr-Weibull, Burr-exponential Poisson, Burr-exponential, Burr-Rayleigh Poisson, Burr-Rayleigh, Burr-Poisson, Burr, Lomax-exponential Poisson, Lomax-Weibull, Lomax-exponential, Lomax-Rayleigh, Lomax-Poisson, Lomax, Weibull, Rayleigh and exponential distributions. Maximum likelihood estimation technique is used to estimate the model parameters followed by a Monte Carlo simulation study. Finally an application of the BWP model to a real data set is presented to illustrate the usefulness of the proposed class of distributions.

Author Biographies

Broderick Oluyede, Georgia Southern University
Broderick O. Oluyede, Ph.D.Professor and Consulting StatisticianDirector, Statistical Consulting Unit (SCU)Department of Mathematical SciencesGeorgia Southern UniversityStatesboro, GA 30460Telephone: (912) 478 5427Email:
Boikanyo Makubate, Botswana International University of Science and Technology

Senior Lecturer

Department of Mathematics and Computational Sciences

Botswana International University of Science and Technology
Palapye, BW

Shujiao Huang, University of Houston

Research Associate

University of Houston


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How to Cite
Oluyede, B., Mdlongwa, P., Makubate, B., & Huang, S. (2018). The Burr-Weibull Power Series Class of Distributions. Austrian Journal of Statistics, 48(1), 1-13.
Special Issue on Lifetime Data Modelling (closed)