Posterior propriety in Bayesian extreme value analyses using reference priors

Posterior Propriety in Bayesian Extreme Value Analyses Using Reference Priors

The Generalized Pareto (GP) and Generalized extreme value (GEV) distributions play an important role in extreme value analyses as models for threshold excesses and block maxima, respectively. For each of these distributions we consider Bayesian inference using “reference” prior distributions (in the general sense of priors constructed using formal rules) for the model parameters, specifically a...

Propriety of posterior distributions arising in categorical and survival models under generalized extreme value distribution

This paper introduces a flexible skewed link function for modeling binary as well as ordinal data with covariates based on the generalized extreme value (GEV) distribution. Extreme value techniques have been widely used in many disciplines relating to risk analysis. However, its application in the binary and ordinal data from a Bayesian context is sparse and its strength as a link function has ...

Heuristics as Bayesian inference under extreme priors

Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent ...

Bayesian Priors and Estimation of Extreme Rainfall in Camborne

Propriety of Intrinsic Priors in Invariant Testing Situations

The Theory of Intrinsic Priors, developed by Berger and Pericchi (1996a,b), is a general method of constructing objective priors for testing and model selection when proper priors are considered for the simpler null hypotheses. When this prior distribution is improper, as is typically the case for Objective Bayesian testing, they suggest approximating the (improper) prior by a sequence of prope...