Discussion meetings

Discussion Meetings are events where articles ('papers for reading') 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. See our guidelines for papers for discussion.

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 a discussion meeting or receive a preprint for each meeting by email.

Click here to watch videos from past discussion meetings.

Click here to submit a discussion paper.

Preprint discussion papers

2017

Research Section Discussion Meeting, Wednesday, 10 May 2017
'Sparse graphs using exchangeable random measures’
François Caron and Emily B Fox
Details

RSS Discussion Meeting, Wednesday, 12 April 2017
'Beyond subjective and objective in statistics'
Andrew Gelman and Christian Hennig
Details

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

Preprints

2017

Research Section Discussion Meeting, Wednesday, 10 May 2017

François Caron and Emily B Fox

‘Sparse graphs using exchangeable random measures’

Statistical network modelling has focused on representing the graph as a discrete structure, namely the adjacency matrix. When assuming exchangeability of this array—which can aid in modelling, computations, and theoretical analysis—the Aldous-Hoover theorem informs us that the graph is necessarily either dense or empty. We instead consider representing the graph as an exchangeable random measure and appeal to the Kallenberg representation theorem for this object. We explore using completely random measures (CRMs) to define the exchangeable random measure and we show how our CRM construction enables us to achieve sparse graphs while maintaining the attractive properties of exchangeability. We relate the sparsity of the graph to the Lévy measure defining the CRM. For a specific choice of CRM, our graphs can be tuned from dense to sparse on the basis of a single parameter. We present a scalable Hamiltonian Monte Carlo algorithm for posterior inference, which we use to analyse network properties in a range of real data sets, including networks with hundreds of thousands of nodes and millions of edges.

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

The preprint is available to download
'Sparse graphs using exchangeable random measures' (PDF)

Code (zip file)


RSS Discussion Meeting, Wednesday, 12 April 2017

Andrew Gelman (Columbia University, New York) and Christian Hennig (University College London)

'Beyond subjective and objective in statistics'

Decisions in statistical data analysis are often justified, criticized or avoided by using concepts of objectivity and subjectivity. We argue that the words ‘objective’ and ‘subjective’ in statistics discourse are used in a mostly unhelpful way, and we propose to replace each of them with broader collections of attributes, with objectivity replaced by transparency, consensus, impartiality and correspondence to observable reality, and subjectivity replaced by awareness of multiple perspectives and context dependence. Together with stability, these make up a collection of virtues that we think is helpful in discussions of statistical foundations and practice. The advantage of these reformulations is that the replacement terms do not oppose each other and that they give more specific guidance about what statistical science strives to achieve. Instead of debating over whether a given statistical method is subjective or objective (or normatively debating the relative merits of subjectivity and objectivity in statistical practice), we can recognize desirable attributes such as transparency and acknowledgement of multiple perspectives as complementary goals. We demonstrate the implications of our proposal with recent applied examples from pharmacology, election polling and socio-economic stratification. The aim of the paper is to push users and developers of statistical methods towards more effective use of diverse sources of information and more open acknowledgement of assumptions and goals.

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

The preprint is available to download
'
Beyond subjective and objective in statistics' (PDF)


Research Section 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)