Inference for a general family of inverted exponentiated distributions under unified hybrid censoring with partially observed competing risks data.

In this paper, statistical inference of a competing risks model is developed based on a unified hybrid censoring scheme when the latent failure times follow a general family of inverted exponentiated distributions, which covers a wide range of lifetime distributions. Point and interval estimation methods for estimating the model parameters based on maximum likelihood and Bayesian approaches under non-restricted and restricted parameter spaces are developed. The Bayes estimates are obtained under the squared error loss function with non-informative and informative prior distributions. The existence and uniqueness of the maximum likelihood estimates of the model parameters are proved. A Monte Carlo simulation study is carried out to evaluate the performance of the proposed estimation procedures. To illustrate the proposed inferential procedures, a real data analysis is provided. Finally, some concluding remarks and future research directions are presented. • Competing risks model based on unified hybrid censoring scheme. • Frequentist and Bayesian approached for statistical inference of a general family of inverted exponentiated distributions. • Establish the uniqueness and existence of the maximum likelihood estimates. • Statistical inference under cases with and without ordered restriction on the shape parameters. • Recommendations for the choice of point and interval estimation procedures based on Monte Carlo simulation results. [ABSTRACT FROM AUTHOR]

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