Classical and Bayesian Inference on Finite Mixture of Exponentiated Kumaraswamy Gompertz and Exponentiated Kumaraswamy Fréchet Distributions under Progressive Type II Censoring with Applications

Refah Alotaibi, Ehab M. Almetwally, Indranil Ghosh and Hoda Rezk
Additional contact information
Refah Alotaibi: Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Ehab M. Almetwally: Department of Mathematical Statistical, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt
Indranil Ghosh: Department of Mathematics and Statistics, University of North Carolina, Wilmington, NC 27599, USA
Hoda Rezk: Department of Statistics, Al-Azhar University, Cairo 11751, Egypt

Mathematics, 2022, vol. 10, issue 9, 1-23

Abstract: A finite mixture of exponentiated Kumaraswamy Gompertz and exponentiated Kumaraswamy Fréchet is developed and discussed as a novel probability model. We study some useful structural properties of the proposed model. To estimate the model parameters under the classical method, we use the maximum likelihood estimation using a progressive type II censoring scheme. Under the Bayesian paradigm the estimation is carried out with gamma priors under a progressive type II censored samples with squared error loss function. To demonstrate the efficiency of the proposed model based on progressively type II censoring, a simulation study is carried out. Three actual data sets are used as an example, demonstrating that the suggested model in the new class fits better than the existing finite mixture models available in the literature.

Keywords: finite mixture; exponentiated Kumaraswamy Gompertz distribution; exponentiated Kumaraswamy Fréchet; Bayesian approach; loss function; progressive type II censoring (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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