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 Judith Shorten.
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.
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 Judith Shorten, 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 firstname.lastname@example.org 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.
Medical Section Ordinary Meeting, Wednesday, June 11th, 2014
R. G. Cowell, T. Graversen, S. L. Lauritzen and J. Mortera
Research Section Ordinary Meeting, Wednesday, October 16th, 2013
K. Frick, A. Munk and H. Sieling
The Address of the President, Wednesday, June 26th, 2013
J. Pullinger (Statistics making an impact) (PDF, 2.62 MB)
The Address of the President, Wednesday, December 7th, 2011
V. Isham (The evolving Society: united we stand) (PDF, 4.14 MB)
The Address of the President, Wednesday, December 10th, 2008
D. J. Hand (Modern statistics: the myth and the magic) (PDF, 1.7 MB)
The Address of the President, Wednesday, December 12th, 2007
D. Tim Holt (Official statistics, public policy and public trust) (PDF, 580 kB)
The Address of the President, Wednesday, June 15th, 2005
A. P. Grieve (The professionalization of the 'shoe clerk') (PDF, 160 kB)
ORDINARY MEETING SPONSORED BY THE MEDICAL SECTION, Wednesday, June 11th, 2014
R. G. Cowell (City University London), T. Graversen and S. L. Lauritzen (University of Oxford) and J. Mortera (Università Roma Tre)
Analysis of forensic DNA mixtures with artefacts
DNA is now routinely used in criminal investigations and court cases, although DNA samples taken at crime scenes are of varying quality and therefore present challenging problems for their interpretation. We present a statistical model for the quantitative peak information obtained from an electropherogram of a forensic DNA sample and illustrate its potential use for the analysis of criminal cases. In contrast with most previously used methods, we directly model the peak height information and incorporate important artefacts that are associated with the production of the electropherogram. Our model has a number of unknown parameters, and we show that these can be estimated by the method of maximum likelihood in the presence of multiple unknown individuals contributing to the sample, and their approximate standard errors calculated; the computations exploit a Bayesian network representation of the model. A case example from a UK trial, as reported in the literature, is used to illustrate the efficacy and use of the model, both in finding likelihood ratios to quantify the strength of evidence, and in the deconvolution of mixtures for finding likely profiles of the individuals contributing to the sample. Our model is readily extended to simultaneous analysis of more than one mixture as illustrated in a case example. We show that the combination of evidence from several samples may give an evidential strength which is close to that of a single-source trace and thus modelling of peak height information provides a potentially very efficient mixture analysis.
To be published in Series C.
Analysis of forensic DNA mixtures with artefacts (PDF, 890 kB) and supporting information file (PDF, 440 kB)
RESEARCH SECTION ORDINARY MEETING, Wednesday, October 16th, 2013
K. Frick (Interstate University of Applied Sciences of Technology, Buchs), A. Munk (University of Goettingen and Max Planck Institute for Biophysical Chemistry, Goettingen) and H. Sieling (University of Goettingen)
Multiscale change point inference
We introduce a new estimator, the simultaneous multiscale change point estimator, for the change point problem in exponential family regression. An unknown step function is estimated by minimizing the number of change points over the acceptance region of a multiscale test at a level alpha. The probability of overestimating the true number of change points K is controlled by the asymptotic null distribution of the multiscale test statistic. Further, we derive exponential bounds for the probability of underestimating K. By balancing these quantities, alpha will be chosen such that the probability of correctly estimating K is maximized. All results are even non-asymptotic for the normal case. On the basis of these bounds, we construct (asymptotically) honest confidence sets for the unknown step function and its change points. At the same time, we obtain exponential bounds for estimating the change point locations which for example yield the minimax rate O(n-1) up to a log-term. Finally, the simultaneous multiscale change point estimator achieves the optimal detection rate of vanishing signals as n tends to infinity, even for an unbounded number of change points. We illustrate how dynamic programming techniques can be employed for efficient computation of estimators and confidence regions. The performance of the multiscale approach proposed is illustrated by simulations and in two cutting edge applications from genetic engineering and photoemission spectroscopy.
To be published in Series B.
Multiscale change point inference, parts one (ZIP 5.3 MB), two (ZIP 4.9 MB) and three (ZIP 4.5 MB)