The Royal Statistical SocietyThe Royal Statistical Society
Preprints of journal discussion papers

The preprints here are available to download to encourage discussion at Ordinary Meetings of the Society before publication in the Journal of the Royal Statistical Society. Certain other papers which will appear in the journal, such as Presidential addresses, are provided for early dissemination of material that may be of wide interest. However, these preprints will typically be removed from this site as soon as they have appeared in print. All preprints available here are provisional and therefore subject to later amendment by the authors. If you would like to receive a preprint for each meeting automatically by e-mail, please contact Charlotte Stovell at journal@rss.org.uk.
 
The meetings are usually held at the Society's premises in London and normally begin at 5.00 p.m., with tea available from 4.30 p.m. Fellows and guests are welcome to attend the free informal drinks reception which follows the Ordinary Meeting at about 7 p.m. The meetings may be preceded by an informal session at 3.00 p.m. on the issues raised by the papers.
 
2010
 
2009
 

Presidential addresses

Discussion at Ordinary Meetings

Contributions to the discussion at Ordinary Meetings are welcome, whether in person at the meeting or subsequently in writing. If you would like to speak at a meeting, please contact Charlotte Stovell at journal@rss.org.uk, preferably at least a week before the date of the meeting. Contributions must not exceed 5 minutes' speaking time and 400 words for publication in the journal (excluding details of any references quoted). In either case, written versions should be sent to the Executive Editor at the Royal Statistical Society, 12 Errol Street, London, EC1Y 8LX, UK, or by e-mail as PostScript or PDF file attachments to journal@rss.org.uk to arrive no later than 2 weeks after the meeting. If time allows, contributions that are received before the day of the meeting may be read out by the Secretary for the meeting on behalf of anyone who cannot attend.
 
 
Preprints
 
ORDINARY MEETING, Wednesday, September 23rd, 2009
 
P. J. Diggle (Lancaster University and Johns Hopkins University School of Medicine, Baltimore), R. Menezes (University of Minho) and T. Su (Lancaster University)
 
Geostatistical inference under preferential sampling 
Geostatistics involves the fitting of spatially continuous models to spatially discrete data. Preferential sampling arises when the process that determines the data locations and the process being modelled are stochastically dependent. Conventional geostatistical methods assume, if only implicitly, that sampling is non-preferential. However, these methods are often used in situations where sampling is likely to be preferential. For example, in mineral exploration, samples may be concentrated in areas that are thought likely to yield high grade ore. We give a general expression for the likelihood function of preferentially sampled geostatistical data and describe how this can be evaluated approximately by using Monte Carlo methods. We present a model for preferential sampling and demonstrate through simulated examples that ignoring preferential sampling can lead to misleading inferences. We describe an application of the model to a set of biomonitoring data from Galicia, northern Spain, in which making allowance for preferential sampling materially changes the results of the analysis.
 
To be published in Series C.
 
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RESEARCH SECTION ORDINARY MEETING, Wednesday, October 14th, 2009
 
C. Andrieu (University of Bristol), A. Doucet and R. Holenstein (University of British Columbia)
 
Particle Markov chain Monte Carlo methods 
 
Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods have emerged as the two main tools to sample from high dimensional probability distributions. Although asymptotic convergence of MCMC algorithms is ensured under weak assumptions, the performance of these algorithms is unreliable when the proposal distributions that are used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. We show how it is possible to build efficient high dimensional proposal distributions by using SMC methods. This allows us not only to improve over standard MCMC schemes but also to make Bayesian inference feasible for a large class of statistical models where this was not previously so. We demonstrate these algorithms on a non-linear state space model and a Lévy-driven stochastic volatility model.
 
To be published in Series B.
 
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RESEARCH  SECTION ORDINARY MEETING, Wednesday, February 3rd, 2010
 
N. Meinshausen (University of Oxford) and P. Bühlmann (Eidgenössiche Technische Hochschule Zürich)
 
Stability selection 
 
Estimation of structure, such as in variable selection, graphical modelling or cluster analysis, is notoriously difficult, especially for high dimensional data. We introduce stability selection. It is based on subsampling in combination with (high dimensional) selection algorithms. As such, the method is extremely general and has a very wide range of applicability. Stability selection provides finite sample control for some error rates of false discoveries and hence a transparent principle to choose a proper amount of regularisation for structure estimation. Variable selection and structure estimation improve markedly for a range of selection methods if stability selection is applied. We prove for the randomized lasso that stability selection will be variable selection consistent even if the necessary conditions needed for consistency of the original lasso method are violated. We demonstrate stability selection for variable selection and Gaussian graphical modelling, using real and simulated data.
 
To be published in Series B.
 
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