If, in the mid 1980's, one had asked the average statistician about the difficulties of using Bayesian Statistics, his/her most likely answer would have been"Well, there is this problem of selecting a prior distribution and then, even if one agrees on the prior, the whole Bayesian inference is...

Abstract only: Today’s data analysts and modellers are in the luxurious position of being able to more closely describe, estimate, predict and infer about complex systems of interest, thanks to ever more powerful computational methods but also wider ranges of modelling distributions. Mixture...

We consider the class of concave distortion risk measures to study how choice is influenced by the decision-maker's attitude to risk and provide comparative static results. We also assume ambiguity about the probability distribution of the risk and consider a framework à la Klibanoff, Marinacci...