Shape Parameter of Weibull Distribution Employed in Extreme Wave Height Statistics

Fracture Strength: Stress Concentration, Extreme Value Statistics, and the Fate of the Weibull Distribution

Zsolt Bertalan, Ashivni Shekhawat, James P. Sethna, and Stefano Zapperi ISI Foundation, Via Alassio 11/c, 10126 Torino, Italy Department of Materials Science and Engineering, University of California, Berkeley, California 94720, USA Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA LASSP, Physics Department, Cornell University, Ithaca, New York 1...

A Comparison of Shape and Scale Estimators of the Two-Parameter Weibull Distribution

Weibull distributions are widely used in reliability and survival analysis. In this paper, different methods to estimate the shape and scale parameters of the two-parameter Weibull distribution have been reviewed and compared, based on the bias, mean square error and variance. Because a theoretical comparison is not possible, an extensive simulation study has been conducted to compare the perfo...

Analytical bounds for the MLE of the Weibull shape parameter

Bayesian Life Test Planning for the Weibull Distribution with Given Shape Parameter

This paper describes Bayesian methods for life test planning with Type II censored data from a Weibull distribution, when the Weibull shape parameter is given. We use conjugate prior distributions and criteria based on estimating a quantile of interest of the lifetime distribution. One criterion is based on a precision factor for a credibility interval for a distribution quantile and the other ...

Bayesian Estimation of Shift Point in Shape Parameter of Inverse Gaussian Distribution Under Different Loss Functions

In this paper, a Bayesian approach is proposed for shift point detection in an inverse Gaussian distribution. In this study, the mean parameter of inverse Gaussian distribution is assumed to be constant and shift points in shape parameter is considered. First the posterior distribution of shape parameter is obtained. Then the Bayes estimators are derived under a class of priors and using variou...