How does the Journal webinar work?
Journal webinars are held every few months and last about an hour. Journal papers are carefully selected from recent issues of the
Royal Statistical Society's journals by editorial board for their importance, relevance and/or use of cutting-edge methodology; and authors are invited to present their work and take questions from an audience who 'dial in' to access the webinar.
Two papers are selected from our journals and authors will be invited to present their papers (20 minutes) followed by discussion (25 minutes) for each paper. Attendees will dial into a teleconference call. Papers and slides will be available to download two weeks in advance or you can log into the conference system and follow the presentation live online. These sessions are open to members and non-members. No need to pre-register. Audio recordings will be available for download shortly after the session.
Questions on the paper or general queries can be emailed in advance of the session to firstname.lastname@example.org.
For UK and international users the link to the Skype for Business app is provided on this page
Wednesday 21st February at 3pm (GMT)
'A Bayesian spatiotemporal model to estimate long-term exposure to outdoor air pollution at coarser administrative geographies in England and Wales’ by Sabyasachi Mukhopadhyay & Sujit K Sahu - Download slides (PDF)
The paper was published online in Series A of the journal in June 2017 . Publishers, Wiley, will make the paper free to access a couple of weeks before and after the event on 21st February.
Estimation of long-term exposure to air pollution levels over a large spatial domain, such as the mainland UK, entails a challenging modelling task since exposure data are often only observed by a network of sparse monitoring sites with variable amounts of missing data. The paper develops and compares several flexible non-stationary hierarchical Bayesian models for the four most harmful air pollutants, nitrogen dioxide and ozone, and PM10 and PM2.5 particulate matter, in England and Wales during the 5-year period 2007–2011. The models make use of observed data from the UK's automatic urban and rural network as well as output of an atmospheric air quality dispersion model developed recently especially for the UK. Land use information, incorporated as a predictor in the model, further enhances the accuracy of the model. Using daily data for all four pollutants over the 5-year period we obtain empirically verified maps which are the most accurate among the competition. Monte Carlo integration methods for spatial aggregation are developed and these enable us to obtain predictions, and their uncertainties, at the level of a given administrative geography. These estimates for local authority areas can readily be used for many purposes such as modelling of aggregated health outcome data and are made publicly available alongside this paper
Presenter: Sujit K Sahu, Professor of Statistics at the University of Southampton
Chair: Richard Chandler, Professor of Statistics, University College London
Discussant: Jonathan Rougier, Professor of Statistical Science, Bristol
Webcasts, MP3s and slides from past events are available to download.