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B1225
Title: Bayesian estimation of the threshold of a generalised Pareto distribution for heavy-tailed observations Authors:  Cristiano Villa - University of Kent (United Kingdom) [presenting]
Abstract: A method is discussed to define prior distributions for the threshold of a generalised Pareto distribution, in particular when its applications are directed to heavy-tailed data, and considering a semi-parmatric model set up. We propose to assign prior probabilities to the order statistics of a given set of observations. In other words, we assume that the threshold coincides to one of the data points. We show two ways of defining a prior: by assigning equal mass to each order statistic, that is a uniform prior, and by considering the worth that every order statistic has in representing the true threshold. Both proposed priors represent a scenario of minimal information, and we study their adequacy through simulation exercises and by analysing two applications from insurance and finance.