In many applications, data exhibit skewness and in this paper we present a new family of density functions modeling skewness based on a transformation, analagous to those of location and scale. Here we note that location will always refer to mode. Hence, in order to model data to include shape,...

Recent papers have been highly promotional of the benefits of machine learning in the detection of corporate fraud. For example, Bao, Ke, Li, Yu, and Zhang (2020) recently published in the Journal of Accounting Research report that their machine learning model increases performance by +75% above...

This paper uses a decision theoretic approach for updating a probability measure representing beliefs about an unknown parameter. A cumulative loss function is considered, which is the sum of two terms: one depends on the prior belief and the other one on further information obtained about the...

A Polya tree is characterised by a special class of predictive probabilities.

This paper introduces a bivariate Dirichlet process for modelling a partially exchangeable sequence of observables. The proposed model would be relevant when two distributions are unknown but are thought to be close to each other. For two random distributions with the same marginals, the belief...