Bayesian likelihood methods for estimating the end point of a distribution

Peter Hall and Julian Z. Wang

Journal of the Royal Statistical Society Series B, 2005, vol. 67, issue 5, 717-729

Abstract: Summary. We consider maximum likelihood methods for estimating the end point of a distribution. The likelihood function is modified by a prior distribution that is imposed on the location parameter. The prior is explicit and meaningful, and has a general form that adapts itself to different settings. Results on convergence rates and limiting distributions are given. In particular, it is shown that the limiting distribution is non‐normal in non‐regular cases. Parametric bootstrap techniques are suggested for quantifying the accuracy of the estimator. We illustrate performance by applying the method to multiparameter Weibull and gamma distributions.

Date: 2005
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