This paper uses free-knot and fixed-knot regression splines in a Bayesian context to develop methods for the nonparametric estimation of functions subject to shape constraints in models with log-concave likelihood functions. The shape constraints we consider include monotonicity, convexity and...

This paper develops a new family of Bayesian semi-parametric models. A particular member of this family is used to model option prices with the aim of improving out-of-sample predictions. A detailed empirical analysis is made for European index call and put options to illustrate the ideas.

This article introduces a new family of Bayesian semiparametric models for the conditional distribution of daily stock index returns. The proposed models capture key stylized facts of such returns, namely, heavy tails, asymmetry, volatility clustering, and the "leverage effect." A Bayesian...

Scale mixtures of uniform distributions are used to model non-normal data in both univariate and multivariate settings. In addition to providing greater modelling flexibility, the use of scale mixtures of uniforms also results in straightforward computational strategies, particularly in a...

A new simulation method, Auxiliary Random Functions, is introduced. When used within a Gibbs sampler, this method enables a unified treatment of exact, right-censored, left-censored, left-trucated and interval censored data, with and without covariates, in survival models. The models and methods...

We discuss the relevance of consistency to the Bayesian. Should consistency be dismissed as irrelevant or thought about seriously when constructing prior distributions? Strong opinions have been held on this matter, but it is probably fair to say it is a largely neglected area. Pioneers, such as...