We consider Bayesian inference via Markov chain Monte Carlo for a variety of fractal Gaussian processes on the real line. These models have unknown parameters in the covariance matrix, requiring inversion of a new covariance matrix at each Markov chain Monte Carlo iteration. The processes have...

The reference prior algorithm [Berger and Bernardo, 1992, Bayesian Statistics 4, Oxford University Press, Oxford, pp. 35-60] is applied to multivariate location-scale models with any regular sampling density, where we establish the irrelevance of the usual assumption of Normal sampling if our...

We represent random vectors Z that take values in n-{0} as Z=RY, where R is a positive random variable and Y takes values in an (n-1)-dimensional space . By fixing the distribution of either R or Y, while imposing independence between them, different classes of distributions on n can be...

Continuous-time stochastic volatility models are becoming an increasingly popular way to describe moderate and high-frequency financial data. Barndorff-Nielsen and Shephard (2001a) proposed a class of models where the volatility behaves according to an Ornstein–Uhlenbeck (OU) process, driven...

We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior...