Estimation and prediction from gamma distribution based on record values

In this paper, we introduce the record values arising from gamma distribution with three parameters. Next, we compute the means, variances and covariances of the lower record values. Then, we use these moments to calculate the best linear unbiased estimates (BLUES) for the location and scale parameters of gamma distribution. By using the BLUES, we construct confidence intervals for the location and scale parameters through Monte Carlo simulations. In addition, we discuss the point and interval prediction for the future records. Abstract: Recently, a new distribution, called generalized exponential distribution (GED), has been introduced and studied quite extensively by authors. The GED can be used as an alternative to gamma and Weibull distributions in many situations. In this article, we use the moments of order statistics from the GED derived by Raqab and Ahsanullah (2001) and Raqab (2004) to develop the correlation goodness-of-fit test for the GED. In addition, we calculate the power of the test based on some other alternative distributions. Further, we construct approximate confidence intervals for the location and scale parameters of the GED. Finally, we apply the procedures developed in the paper to real data set.