Discussion papers at Ordinary Meetings

Ordinary 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 Ordinary 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.

Preprint discussion papers

2016

Research Section Ordinary Meeting, Wednesday, 5 October 2016
‘A Bayesian information criterion for singular models’
Mathias Drton, University of Washington, Seattle, USA
Martyn Plummer, International Agency for Research on Cancer, Lyon, France
Details

General Applications Section Ordinary Meeting, Tuesday, 6 September 2016 at University Place, Manchester University, as part of the RSS 2016 Conference. 
'Should we sample a time series more frequently? Decision support via multirate spectrum estimation' 
Guy P Nason and Ben Powell, University of Bristol, UK
Duncan Elliott, Office for National Statistics, Newport, UK
Paul A Smith, University of Southamption, UK
Details

Official Statistics Ordinary Meeting, Wednesday, 15 June 2016
‘New statistics for old?—measuring the wellbeing of the UK’
Paul Allin and David J Hand
Details

Research Section Ordinary Meeting, Wednesday, 11 May 2016
Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann and Nicolai Meinshausen
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


2016

Research Section Ordinary Meeting, Wednesday, 5 October 2016
Mathias Drton, University of Washington, Seattle, USA
Martyn Plummer, International Agency for Research on Cancer, Lyon, France

'A Bayesian information criterion for singular models'

We consider approximate Bayesian model choice for model selection problems that involve models whose Fisher information matrices may fail to be invertible along other competing submodels. Such singular models do not obey the regularity conditions underlying the derivation of Schwarz’s Bayesian information criterion BIC and the penalty structure in BIC generally does not reflect the frequentist large sample behaviour of their marginal likelihood. Although large sample theory for the marginal likelihood of singular models has been developed recently, the resulting approximations depend on the true parameter value and lead to a paradox of circular reasoning. Guided by examples such as determining the number of components of mixture models, the number of factors in latent factor models or the rank in reduced rank regression, we propose a resolution to this paradox and give a practical extension of BIC for singular model selection problems.

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

The preprint is available to download

A Bayesian information criterion for singular models (PDF)

General Applications Section Ordinary Meeting, Tuesday, 6 September 2016 at University Place, Manchester University, as part of the RSS 2016 Conference. 
Guy P Nason and Ben Powell, University of Bristol, UK
Duncan Elliott, Office for National Statistics, Newport, UK
Paul A Smith, University of Southamption, UK

'Should we sample a time series more frequently? Decision support via multirate spectrum estimation'

Suppose that we have a historical time series with samples taken at a slow rate, e.g. quarterly. The paper proposes a new method to answer the question: is it worth sampling the series at a faster rate, e.g. monthly? Our contention is that classical time series methods are designed to analyse a series at a single and given sampling rate with the consequence that analysts are not often encouraged to think carefully about what an appropriate sampling rate might be. To answer the sampling rate question we propose a novel Bayesian method that incorporates the historical series, cost information and small amounts of pilot data sampled at the faster rate. The heart of our method is a new Bayesian spectral estimation technique that is capable of coherently using data sampled at multiple rates and is demonstrated to have superior practical performance compared with alternatives. Additionally, we introduce a method for hindcasting historical data at the faster rate. A freeware R package, regspec, is available that implements our methods. We illustrate our work by using official statistics time series including the UK consumer price index and counts of UK residents travelling abroad, but our methods are general and apply to any situation where time series data are collected.

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

The preprint is available to download

Should we sample a time series more frequently? Decision support via multirate spectrum estimation (PDF)

Official Statistics Section Ordinary Meeting, Wednesday, 15 June 2016
Paul Allin and David J Hand (Imperial College London, UK)

‘New statistics for old?—measuring the wellbeing of the UK’

Attempts to create measures of national wellbeing and progress have a long history. In the UK, they go back at least as far as the 1790s, with Sir John Sinclair’s Statistical Account of Scotland. More recently, worldwide interest has led to the creation of various indices seeking to go beyond familiar economic measures like gross domestic product. We review the ‘Measuring national well-being’ development programme of the UK’s Office for National Statistics and explore some of the challenges which need to be faced to bring wider measures into use. These include the importance of getting the measures adopted as policy drivers, how to challenge the continuing dominance of economic measures, sustainability and environmental issues, international comparability and methodological statistical questions.

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

The preprint is available to download.

New statistics for old?—measuring the wellbeing of the UK’ (PDF)
Watch Video (YouTube) 

Research Section Ordinary Meeting, Wednesday, 11 May 2016
Jonas Peters (MPI for Intelligent Systems, Tübingen, and Eidgenössiche Technische Hochschule Zürich) and Peter Bühlmann and Nicolai Meinshausen (Eidgenössiche Technische Hochschule Zürich)

‘Causal inference by using invariant prediction: identification and confidence intervals’

What is the difference between a prediction that is made with a causal model and that with a non-causal model? Suppose that we intervene on the predictor variables or change the whole environment. The predictions from a causal model will in general work as well under interventions as for observational data. In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on variables. Here, we propose to exploit this invariance of a prediction under a causal model for causal inference: given different experimental settings (e.g. various interventions) we collect all models that do show invariance in their predictive accuracy across settings and interventions. The causal model will be a member of this set of models with high probability. This approach yields valid confidence intervals for the causal relationships in quite general scenarios. We examine the example of structural equation models in more detail and provide sufficient assumptions under which the set of causal predictors becomes identifiable. We further investigate robustness properties of our approach under model misspecification and discuss possible extensions. The empirical properties are studied for various data sets, including large-scale gene perturbation experiments.

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

The preprint is available to download.
‘Causal inference by using invariant prediction: identification and confidence intervals’ (PDF)
Watch video (YouTube)