by K.J. McManus, D. A. Stephens, N. M. Adams, S. A. Islam, P. S. Freemontand M. J. Hendzel

(Department of Mathematics and Division of Molecular Biosciences, Imperial College London, and Department of Oncology, University of Alberta)

Summary: A spatial analysis of interesting cell biological data set. Submitted.

November 9th 2005

by D. A. Stephens and M. J. Crowder.

Summary: A paper outlining a Bayesian solution for a large missing data problem, following up some earlier work of ours.  Under revision.

October 2005

by E. E. M. Moodie, Department of Biostatistics, University of Washington, Seattle,  D. A. Stephens, Department of Mathematics, Imperial College London, C. E. Stewart, A. R. Fielder, M. J. Moseley. Department of Optometry and Visual Science, City University, London,

Summary: Applied paper with a bit of classical and Bayesian linear mixed modelling and a bit of causal analysis, in a study of childhood amblyopia.

September 2005

by E. E. M. Moodie, Department of Biostatistics, University of Washington, Seattle,  T. S. Richardson, Department of Statistics, University of Washington, Seattle, D. A. Stephens, Department of Mathematics, Imperial College London,

Summary: A paper comparing and contrasting the two principal methods of estimating optimal treatment regimes, In revision.

November 09 2005  Previous Version September 2005

by K. Platanioti, E. J. McCoy and D. A. Stephens.

Summary: A review of approaches to the modelling of univariate and multivariate volatility processes.  In revision.

Population-based reversible jump Markov chain Monte Carlo

by A. Jasra, D. A. Stephens and C. C. Holmes.  Submitted

Summary: Some theory for population MCMC methods, and an application.

Interacting Sequential Monte Carlo Samplers for Trans-dimensional Simulation

by A. Jasra, D. A. Stephens, A Doucet and C. C. Holmes

Summary: A paper on SMC for transdimensional problems, with technical results obtained via Feynman-Kac representations.

On Population-Based Simulation for Static Inference

by A. Jasra, D. A. Stephens and C. C. Holmes.  Submitted

Summary: A comparison of population MCMC and population SMC.

Stochastic Volatility Modelling with General Marginal Distributions: Inference, Prediction and Model Selection For Option Pricing.

by M. P. S. Gander and D. A. Stephens. Submitted to JRSSB October 2004, rejected August 2005. Submitted.

Summary:  A paper studying MCMC inference for general versions of the Barndorff-Nielsen & Shephard stochastic volatility model with applications to option pricing (the paper was rejected by RSSB,)

by M. P. S. Gander and D. A. Stephens. In revision.

Summary:  A paper looking at non-linear, non-Gaussian AND non OU  volatility process models.

Summary:  A method for hierarchical co-clustering of the mosquito gene expression data

by N. A. Heard, C. C. Holmes, D. A. Stephens, To appear in JASA

Summary: A new Bayesian hierarchical clustering method, and an application to the analysis of mosquito gene expression data.

Summary: A Paper on mixtures and label switching; a survey of approaches, with a bit of a critique.

Bayesian Mixture Modelling in Geochronology via Markov chain Monte Carlo

by A. Jasra, D. A. Stephens, K. L. Gallagher and C. C. Holmes.  To appear in Mathematical Geology.

Summary:  Applied paper doing a mixture analysis in a geological problem.