When a number of distinct models contend for use in prediction, the choice of a single model can offer rather unstable predictions. In regression, stochastic search variable selection with Bayesian model averaging offers a cure for this robustness issue but at the expense of requiring very many...

Consider multivariate linear calibration of a single standard. We show that a selection of the q' most informative of the q responses gives a finite mean squared error for the generalised least squares prediction of an unknown standard provided 1 [less-than-or-equals, slant] q'...

This paper describes a Bayesian nonparametric approach to volatility estimation. Volatility is assumed to follow a superposition of an infinite number of Ornstein--Uhlenbeck processes driven by a compound Poisson process with a parametric or nonparametric jump size distribution. This model...