Goodness-of-fit Tests based on Lorenz Curve for Progressive Censored Data from Normal Distribusion

The problem of examining how well a assumed distribution fits the data of a sample is of significant that has to be examined prior to any inferential process. In this paper, a new goodness-of-fit test for an normal distribution based on progressive censored data is proposed. Using Monte Carlo simulation studies, the present researchers have observed that the proposed test for normality is consistent and quite powerful in comparison with existing goodness-of-fit tests based on progressive censored data. Also, the new test statistic for a real data set is used and the results show that our new test statistic performs well.