Discussion meetings

Discussion Meetings are events where articles appearing in the Journal of the Royal Statistical Society are presented and discussed. The discussion and authors' replies are then published in the relevant Journal series. About half of the meetings are organised by the Society's Research Section and the events are often preceded by an informal session on the issues raised by the papers.

Preprints of journal papers are available to download to encourage discussion at our Discussion Meetings before publication in one of our journals. Other papers, such as Presidential addresses, are also available to download. All preprints available here are provisional and subject to later amendment by the authors.

Contact Judith Shorten if you would like to make a written contribution to the meeting or receive a preprint for each meeting by email.

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Preprint discussion papers

2017

RSS Discussion Meeting, Wednesday 15 March 2017
'Random-projection ensemble classification’
Timothy I. Cannings and Richard J. Samworth, University of Cambridge, UK
Details

Presidential addresses

The Address of the president, Wednesday 24 June 2015
P J Diggle
Download ‘Statistics: a data science for the 21st century’ (PDF)

The Address of the president, Wednesday 26 June 2013
J Pullinger
Download ‘Statistics making an impact’ (PDF)

The Address of the president, Wednesday 7 December 2011
V Isham
Download ‘The evolving Society: united we stand’ (PDF)

The Address of the president, Wednesday 10 December 2008
D J Hand
Download ‘Modern statistics: the myth and the magic’ (PDF)

The Address of the president, Wednesday 12 December 2007
D Tim Holt
Download ‘Official statistics, public policy and public trust’ (PDF)

The Address of the president, Wednesday 15 June 2005
A P Grieve
Download ‘The professionalization of the shoe clerk’ (PDF)

Preprints


2017

RSS Discussion Meeting, Wednesday 15 March 2017
Timothy I. Cannings and Richard J. Samworth, University of Cambridge, UK

'Random-projection ensemble classification’

We introduce a very general method for high dimensional classification, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower dimensional space. In one special case that we study in detail, the random projections are divided into disjoint groups, and within each group we select the projection yielding the smallest estimate of the test error. Our random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment. Our theoretical results elucidate the effect on performance of increasing the number of projections. Moreover, under a boundary condition that is implied by the sufficient dimension reduction assumption, we show that the test excess risk of the random-projection ensemble classifier can be controlled by terms that do not depend on the original data dimension and a term that becomes negligible as the number of projections increases. The classifier is also compared empirically with several other popular high dimensional classifiers via an extensive simulation study, which reveals its excellent finite sample performance.

To be published in Series B, for more information go to the Wiley Online Library.

The preprint is available to download

Random-projection ensemble classification’ (PDF) 
Supporting information (PDF)
Data and code (Zip file)