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Research highlights

Here are links to just a few of the group's recent research outputs in statistical theory and methodology

Monte Carlo methods

Particle Markov chain Monte Carlo methods
by Christophe Andrieu, Arnaud Doucet and Roman Holenstein

The pseudo-marginal approach for efficient Monte Carlo computations
by Christophe Andrieu and Gareth Roberts

Monte Carlo filtering of piecewise deterministic processes
by Nick Whiteley, Adam Johansen and Simon Godsill

Nonparametric methods in regression and time series

Using SIMEX for smoothing-parameter choice in errors-in-variables problems
by Aurore Delaigle and Peter Hall

Bivariate hard thresholding in wavelet function estimation
by Piotr Fryzlewicz

A wavelet-Fisz approach to spectrum estimation
by Piotr Fryzlewicz, Guy Nason and Rainer von Sachs

Nonparametric regression, confidence regions and regularization
by Laurie Davies, Arne Kovac and Monika Meise

Multiscale methods for data on graphs and irregular multidimensional situations
by Martin Jansen, Guy Nason and Bernard Silverman

Bayesian modelling and analysis

Graphical models for marked point processes based on local independence
by Vanessa Didelez

A statistical approach to the problem of restoring damaged and contaminated images
by Richard Everitt and Richard Glendinning

Sensitivity of inferences in forensic genetics to assumptions about founding genes
by Peter Green and Julia Mortera

Reified Bayesian modelling and inference for physical systems
by Michael Goldstein and Jonathan Rougier

A general approach to heteroscedastic linear regression
by David Leslie, Robert Kohn and David J. Nott

Mean field inference for the Dirichlet process mixture model
by Oliver Zobay

Other work

Theoretical properties of Cook's PFC dimension reduction algorithm for linear regression
by Oliver Johnson