Goodness of fit Tests for Generalized Frechet Distribution

An important problem in statistics is to obtain information about the form of the population from which the sample is drawn. Goodness of fit test is employed to determine how well the observed sample data "fits" some proposed model. The standard goodness of fit statistics are inappropriate when the parameters of the hypothesized distribution are estimated from the data used for the test. In this paper, we obtain the tables of critical values of modified Kolmogorov-Smirnov (KS) test, Cramer-Von Mises (CVM) test, Anderson-Darling (AD) and Watson test for generalized Frechet distribution with unknown parameters. The sampling distributions for these tests statistics are investigated. This article uses Monte Carlo and Pearson system techniques to create tables of critical values for such situations. Further more, we present power comparison between KS test, CVM test, AD test and Watson test. We used the software package MathCAD (2001) for all programs