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Bayesian likelihood methods for estimating the end point of a distribution

Hall, Peter; Wang, Julian


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...[Show more]

CollectionsANU Research Publications
Date published: 2005
Type: Journal article
Source: Journal of the Royal Statistical Society Series B
DOI: 10.1111/j.1467-9868.2005.00523.x


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