Exponentiated Generalized Inverse Flexible Weibull Distribution: Bayesian and Non-Bayesian Estimation Under Complete and Type II Censored Samples with Applications

In this paper, a new 4-parameter exponentiated generalized inverse flexible Weibull distribution is proposed. Some of its statistical properties are studied. The aim of this paper is to estimate the model parameters via several approaches, namely, maximum likelihood, maximum product spacing and Bayesian. According to Bayesian approach, several techniques are used to get the Bayesian estimators, namely, standard error function, Linex loss function and entropy loss function. The estimation herein is based on complete and censored samples. Markov Chain Monte Carlo simulation is used to discuss the behavior of the estimators for each approach. Finally, two real data sets are analyzed to obtain the flexibility of the proposed model.