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

Official Statistics Section Discussion Meeting, Wednesday, 15 November 2017
‘Statistical challenges of administrative and transaction data’
David J Hand
Details

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

Preprints

2017

Official Statistics Section Discussion Meeting, Wednesday, 15 November 2017

David J Hand

‘Statistical challenges of administrative and transaction data’

Administrative data are becoming increasingly important. They are typically the side effect of some operational exercise and are often seen as having significant advantages over alternative sources of data. Although it is true that such data have merits, statisticians should approach the analysis of such data with the same cautious and critical eye as they approach the analysis of data from any other source. The paper identifies some statistical challenges, with the aim of stimulating debate about and improving the analysis of administrative data, and encouraging methodology researchers to explore some of the important statistical problems which arise with such data.

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

The preprint is available to download
'Statistical challenges of administrative and transaction data' (PDF)


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)