Discussion meetings
Discussion meetings are events where articles ('papers for reading') appearing in the Journal of the RSS are presented and discussed. The discussion and authors' replies are then published in the relevant Journal series.
Read more about our discussion meetings, including guidelines for papers for discussion.
Contact Judith Shorten if you would like to make a written contribution to a discussion meeting or join our mailing list for an early invitation to future meetings.
Next Discussion Meeting
'Some statistical aspects of the Covid-19 response'
Will take place at Hallam Conference Centre and online
Hallam Conference Centre,
44 Hallam St, London W1W 6JJ
Thursday, 10 April 2025
Time: 3pm to 5pm (UK time)
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Register online
Paper: 'Some statistical aspects of the Covid-19 response'
Authors: Simon N. Wood, School of Mathematics, University of Edinburgh, UK, Ernst C. Wit, Institute of Computing, Università della Svizzera Italiana, Lugano, Switzerland, Paul M. McKeigue, College of Medicine and Veterinary Medicine, University of Edinburgh, U.K, Danshu Hu, Beth Flood, Lauren Corcoran and Thea Abou Jawad, School of Mathematics, University of Edinburgh, UK.
Download the preprint
Supplementary R data package
Abstract: This paper discusses some statistical aspects of the U.K. Covid-19 pandemic response, focusing particularly on cases where we believe that a statistically questionable approach or presentation has had a substantial impact on public perception, or government policy, or both. We discuss the presentation of statistics relating to Covid risk, and the risk of the response measures, arguing that biases tended to operate in opposite directions, overplaying Covid risk and underplaying the response risks. We also discuss some issues around presentation of life loss data, excess deaths and the use of case data. The consequences of neglect of most individual variability from epidemic models, alongside the consequences of some other statistically important omissions are also covered. Finally the evidence for full stay at home lockdowns having been necessary to reverse waves of infection is examined, with new analyses provided for a number of European countries.
'New tools for network time series with an application to COVID-19 hospitalisations'
Will take place at Imperial College London, and online.
Imperial College London
Huxley Building (Lecture Theatre 130)
180 Queen’s Gate,
South Kensington Campus
London SW7 2AZ
Tuesday, 10 June 2025
Time: 3pm to 5pm (UK time)
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Paper: 'New tools for network time series with an application to COVID-19 hospitalisations'
Authors: Guy Nason, Imperial College London, Daniel Salnikov, Imperial College London, Mario Cortina-Borja, University College London, Institute of Child Health.
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Supplementary material
Abstract: Network time series models are increasingly important across many areas, involving known or inferred underlying network structure, which can be exploited to make sense of high-dimensional dynamic phenomena.
We introduce two new association measures: the network and partial network autocorrelation functions and define Corbit (correlation--orbit) visualisation plots. Corbit plots permit interpretation of underlying correlation structures and, crucially, aid model selection more rapidly than general tools such as typical information criteria.
We introduce interpretations of generalised network autoregressive (GNAR) processes as generalised graphical models. We shine new light on how incorporating prior information is related to variable selection and shrinkage in the GNAR context.
We illustrate the usefulness of GNAR models, network autocorrelations and Corbit plots for a novel network time series modelling of COVID--19 mechanical ventilation bed occupancies at 140 NHS Trusts.
We also introduce the R--Corbit plot that shows correlations over different time periods or with respect to external covariates and plots that quantify the relevance and influence of individual nodes. Our analysis provides insight on the COVID--19 series’ underlying dynamics, highlights two groups of geographically co--located `relevant' NHS Trusts, and demonstrates excellent predictive performance.
The paper will be published in Journal of the Royal Statistical Society Series A: Statistics in Society | Oxford Academic (oup.com)'
Past Discussion Meetings
'Methods for Estimating the Exposure-Response Curve to Inform the New Safety Standards for Fine Particulate Matter'
Took place at the Imperial College London and online
Imperial College London
Huxley Building (Lecture Theatre 130)
180 Queen’s Gate,
South Kensington Campus
London SW7 2AZ
Thursday, 12 December 2024
Time: 2pm to 4pm
Register in person
Register online
Paper: 'Methods for Estimating the Exposure-Response Curve to Inform the New Safety Standards for Fine Particulate Matter'
Authors: Michael Cork, Harvard University, USA (presenting), Daniel Mork, Francesca Dominici Harvard University, USA (co-authors)
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Abstract: Exposure to fine particulate matter (PM2.5) poses significant health risks and accurately determining the shape of the relationship between PM2.5 and health outcomes has crucial policy implications. Although various statistical methods exist to estimate this exposure-response curve (ERC), few studies have compared their performance under plausible data-generating scenarios.
This study compares seven commonly used ERC estimators across 72 exposure-response and confounding scenarios via simulation. Additionally, we apply these methods to estimate the ERC between long-term PM2.5 exposure and all-cause mortality using data from over 68 million Medicare beneficiaries in the United States. Our simulation indicates that regression methods not placed within a causal inference framework are unsuitable when anticipating heterogeneous exposure effects. Under the setting of a large sample size and unknown ERC functional form, we recommend utilizing causal inference methods that allow for nonlinear ERCs.
In our data application, we observe a nonlinear relationship between annual average PM2.5 and all-cause mortality in the Medicare population, with a sharp increase in relative mortality at low PM2.5 concentrations. Our findings suggest that stricter limits on PM2.5 could avert numerous premature deaths. To facilitate the utilization of our results, we provide publicly available, reproducible code on Github for every step of the analysis.
The paper will be published in Journal of the Royal Statistical Society Series A: Statistics in Society | Oxford Academic (oup.com)
'Analysis of citizen science data' (multi-paper Discussion meeting)
The Royal Statistical Society is pleased to invite you to the discussion of three papers at the annual conference in Brighton on 3 September, 4:45pm to 6:45pm. It is free to attend and open to members and non-members.
The event will be chaired by our President, Andrew Garrett.
Paper 1: 'Efficient statistical inference methods for assessing changes in species'
Authors: Emily B Dennis12, Alex Diana3, Eleni Matechou2, Byron J T Morgan2
(1Butterfly Conservation, 2University of Kent, 3University of Essex)
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Supplementary materials
Abstract: The global decline of biodiversity, driven by habitat degradation and climate breakdown, is a significant concern. Accurate measures of change are crucial to provide reliable evidence of species’ population changes. Meanwhile citizen science data have witnessed a remarkable expansion in both quantity and sources and serve as the foundation for assessing species’ status. The growing data reservoir presents opportunities for novel and improved inference but often comes with computational costs: computational efficiency is paramount, especially as regular analysis updates are necessary. Building upon recent research, we present illustrations of computationally efficient methods for fitting new models, applied to three major citizen science data sets for butterflies. We extend a method for modelling abundance changes of seasonal organisms, firstly to accommodate multiple years of count data efficiently, and secondly for application to counts from a snapshot mass-participation survey. We also present a variational inference approach for fitting occupancy models efficiently to opportunistic citizen science data. The continuous growth of citizen science data offers unprecedented opportunities to enhance our understanding of how species respond to anthropogenic pressures. Efficient techniques in fitting new models are vital for accurately assessing species’ status, supporting policy-making, setting measurable targets, and enabling effective conservation efforts.
Paper 2: 'Frequentist Prediction Sets for Species Abundance using Indirect Information'
Authors: Elizabeth Bersson and Peter D Hoff, Duke University, Durham, USA
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Abstract: Citizen science databases that consist of volunteer-led sampling efforts of species communities are relied on as essential sources of data in ecology. Summarising such data across counties with frequentist-valid prediction sets for each county provides an interpretable comparison across counties of varying size or composition. As citizen science data often feature unequal sampling efforts across a spatial domain, prediction sets constructed with indirect methods that share information across counties may be used to improve precision. In this article, we present a nonparametric framework to obtain precise prediction sets for a multinomial random sample based on indirect information that maintain frequentist coverage guarantees for each county. We detail a simple algorithm to obtain prediction sets for each county using indirect information where the computation time does not depend on the sample size and scales nicely with the number of species considered. The indirect information may be estimated by a proposed empirical Bayes procedure based on information from auxiliary data. Our approach makes inference for under-sampled counties more precise, while maintaining area-specific frequentist validity for each county. Our method is used to provide a useful description of avian species abundance in North Carolina, USA based on citizen science data from the eBird database.
Paper3: 'Extreme-value modelling of migratory bird arrival dates: Insights from citizen science data'
Authors: Jonathan Koh, University of Bern, Switzerland and Thomas Opitz, INRAE, France
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Abstract: Citizen science mobilises many observers and gathers huge datasets but often without strict sampling protocols, resulting in observation biases due to heterogeneous sampling effort, which can lead to biased statistical inferences. We develop a spatiotemporal Bayesian hierarchical model for bias-corrected estimation of arrival dates of the first migratory bird individuals at a breeding site. Higher sampling effort could be correlated with earlier observed dates. We implement data fusion of two citizen-science datasets with fundamentally different protocols (BBS, eBird) and map posterior distributions of the latent process, which contains four spatial components with Gaussian process priors: species niche; sampling effort; position and scale parameters of annual first arrival date. The data layer includes four response variables: counts of observed eBird locations (Poisson); presence-absence at observed eBird locations (Binomial); BBS occurrence counts (Poisson); first arrival dates (Generalised Extreme-Value). We devise a Markov Chain Monte Carlo scheme and check by simulation that the latent process components are identifiable. We apply our model to several migratory bird species in the northeastern United States for 2001–2021 and find that the sampling effort significantly modulates the observed first arrival date. We exploit this relationship to effectively bias-correct predictions of the true first arrivals.
The papers will be published in Journal of the Royal Statistical Society Series A: Statistics in Society | Oxford Academic (oup.com)
'Inference for extreme spatial temperature events in a changing climate with application to Ireland'
Monday, 3 June 2024, 3-4pm
Online
Paper: 'Inference for extreme spatial temperature events in a changing climate with application to Ireland'.
Download the preprint
Authors: Dáire Healy, Jonathan Tawn, Peter Thorne and Andrew Parnell.
Abstract:
We investigate the changing nature of the frequency, magnitude, and spatial extent of extreme temperatures in Ireland from 1942 to 2020. We develop an extreme value model that captures spatial and temporal non-stationarity in extreme daily maximum temperature data. We model the tails of the marginal variables using the generalised Pareto distribution and the spatial dependence of extreme events by a semi-parametric Brown-Resnick r-Pareto process, with parameters of each model allowed to change over time. We use weather station observations for modelling extreme events since data from climate models (not conditioned on observational data) can over-smooth these events and have trends determined by the specific climate model configuration. However, climate models do provide valuable information about the detailed physiography over Ireland and the associated climate response. We propose novel methods which exploit the climate model data to overcome issues linked to the sparse and biased sampling of the observations. Our analysis identifies a temporal change in the marginal behaviour of extreme temperature events over the study domain, which is much larger than the change in mean temperature levels over this time window. We illustrate how these characteristics result in increased spatial coverage of the events that exceed critical temperatures.
The paper will be published in
Journal of the Royal Statistical Society Series C: ScholarOne Manuscripts (manuscriptcentral.com)
'Independent Review of the UK Statistics Authority' by Denise Lievesley
Took place at the RSS building (Errol St., London) and online
Wednesday, 22 May, 2024
Time: 4pm
Author: Denise Lievesley
Chair: Andrew Garrett, RSS President
Paper: The discussion will be based on the published review and the government's response to it: Independent Review of the UK Statistics Authority 2023 - 2024 - GOV.UK (www.gov.uk)
Safe Testing
Wednesday, 24 January 2024, 4-6pm
Taking place at the RSS building (Errol St., London) and online
Paper: 'Safe Testing'
Authors: Peter Grünwald, CWI and Leiden University, Netherlands, Rianne de Heide, Vrije Universiteit Amsterdam, Netherlands, Wouter Koolen, CWI and University of Twente, Netherlands.
Download the preprint
Abstract
We develop the theory of hypothesis testing based on the e-value, a notion of evidence that, unlike the p-value, allows for effortlessly combining results from several studies in the common scenario where the decision to perform a new study may depend on previous outcomes. Tests based on e-values are safe, i.e. they preserve Type-I error guarantees, under such optional continuation. We define growth rate optimality (GRO) as an analogue of power in an optional continuation context, and we show how to construct GRO e-variables for general testing problems with composite null and alternative, emphasizing models with nuisance parameters. GRO e-values take the form of Bayes factors with special priors. We illustrate the theory using several classic examples including a one-sample safe t-test and the 2 × 2 contingency table. Sharing Fisherian, Neymanian and Jeffreys-Bayesian interpretations, e-values may provide a methodology acceptable to adherents of all three schools.
The paper will be published in Journal of the Royal Statistical Society Series B: Statistical Methodology | Oxford Academic (oup.com)
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