Kumaraswamy log-logistic Weibull distribution: model, theory and application to lifetime and survival data
P. Mdlongwa,
B.O. Oluyede,
A.K.A. Amey,
A.F. Fagbamigbe,
B. Makubate
Affiliations
P. Mdlongwa
Department of Mathematics and Statistical Sciences, Botswana International University of Science and Technology, Palapye, Botswana; Department of Statistics and Operations Research, National University of Science and Technology, Bulawayo, Zimbabwe
B.O. Oluyede
Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA, 30460, USA; Corresponding author.
A.K.A. Amey
Department of Mathematics and Statistical Sciences, Botswana International University of Science and Technology, Palapye, Botswana
A.F. Fagbamigbe
Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, University of Ibadan, Nigeria; Department of Mathematics and Statistical Sciences, Botswana International University of Science and Technology, Palapye, Botswana
B. Makubate
Department of Mathematics and Statistical Sciences, Botswana International University of Science and Technology, Palapye, Botswana
We develop the new Kumaraswamy Log-Logistic Weibull (KLLoGW) distribution by combining the Kumaraswamy and Log-logistic Weibull distributions. This new model is flexible for modelling lifetime data. Some statistical properties including quantile function, hazard rate function, moments and conditional moments are presented. Model parameters are estimated via the method of maximum likelihood and a Monte Carlo simulation study conducted to assess the accuracy of the estimates. Finally, the model is applied to a real dataset.
