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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 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. 


Research Section Ordinary Meeting, Wednesday, October 16th, 2013
K. Frick, A. Munk and H. Sieling

Presidential addresses

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)


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)


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