Goodness-of-fit tests for progressively Type-II censored data from location-scale distributions

In this article, we propose several goodness-of-fit methods for locationscale families of distributions under progressively Type-II censored data. The new tests are based on order statistics and sample spacings. We assess the performance of the proposed tests for the normal and Gumbel models against several alternatives by means of Monte Carlo simulations. It has been observed that the proposed tests are quite powerful in comparison with an existing goodness-of-fit test proposed for progressively Type-II censored data by Balakrishnan et al. [Goodness-of-fit tests based on spacings for progressively Type-II censored data from a general locationscale distribution, IEEE Trans. Reliab. 53 (2004), pp. 349356]. Finally, we illustrate the proposed goodness-of-fit tests using two real data from reliability literature.