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

Abstract

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 gener-
al entropy loss function. 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.