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Full-Text Articles in Physical Sciences and Mathematics
Trans-Dimensional Metropolis-Hastings Using Parallel Chains, Sally Wood, James Pullen, Robert Kohn, David Leslie
Trans-Dimensional Metropolis-Hastings Using Parallel Chains, Sally Wood, James Pullen, Robert Kohn, David Leslie
Sally Wood
A general Bayesian sampling method is developed that uses parallel chains to select between models and to average the predictive density over such models. The method applies to both non-nested models and to nested models, and is particularly useful for mixtures of complex component models, where a novel approach to overcome the label-switching problem is used. The method is illustrated with real and simulated data in model-averaging over alternative financial time series models, mixtures of normal distributions, and mixtures of smoothing spline models.
Priors For A Bayesian Analysis Of Extreme Values, Sally Wood, Julian Wang
Priors For A Bayesian Analysis Of Extreme Values, Sally Wood, Julian Wang
Sally Wood
This article proposes a new prior specification for a Bayesian analysis of the k largest order statistics model. We show that using Jeffreys priors for the end-point and shape parameters of the k largest order statistics model leads to biased estimates of the shape parameter for small to medium sample sizes and to the posterior mode of the end-point being equal to the most extreme observed value. We propose a conjugate prior for the shape parameter and a prior for the end-point which removes the posterior mode at the most extreme observed value while remaining uninformative for values of the …