A General Purpose Approximate Goodness-of-Fit Test for Progressively Type-II Censored Data

We propose a general purpose approximate goodness-of-fit test that covers several families of distributions under progressive Type-II censored data. The test procedure is based on the empirical distribution function (EDF), and generalizes the goodness-of-fit test proposed by Chen and Balakrishnan [11] to progressively Type-II censored data. The new method requires some tables for critical values, which are constructed by Monte Carlo simulation. The power of the proposed tests are then assessed for several alternative distributions, while testing for normal, Gumbel, and log-normal distributions, through Monte Carlo simulations. It is observed that the proposed tests are quite powerful when compared to an existing goodness-of-fit test proposed for progressively Type-II censored data due to Balakrishnan et al. [8]. The proposed goodness-of-fit test is then illustrated with two real data sets.