Estimation for inverse Gaussian Distribution Under First-failure Progressive Hybird Censored Samples

In this paper, a first-failure progressive hybird censoring scheme is introduced that combines progressive first-failure censoring and Type-I censoring. We obtain the maximum likelihood estimators (MLEs) and the Bayes estimators of the unknown parameters from the inverse Gaussian distribution based on the first-failure progressive hybird censoring scheme. The Bayes estimates are computed under squared error, Linex and general entropy loss functions. The asymptotic confidence intervals and coverage probabilities for the parameters are obtained based on the observed Fisher's information matrix. Also, highest posterior density credible intervals for the parameters are computed using Gibbs sampling procedure. A Monte Carlo simulation study is conducted in order to compare the Bayes estimators with the MLEs. Real life data sets are provided to illustration purposes.