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 receive a preprint for each meeting by email.

Forthcoming discussion papers

Research Section Online Interactive Discussion Meeting (rescheduled from April 2020)
Wednesday 24 June, 5-7pm
DeMO pre-meeting, 3:30-4:30pm with Paul Fearnhead and Murray Pollock

The discussion paper 'Quasi-stationary Monte Carlo methods and the ScaLE algorithm' will be presented by Murray Pollock, Paul Fearnhead, Adam M Johansen and Gareth O Roberts. 

You will need to register in advance to obtain joining instructions. Registration closes at 2pm on 24 June.

Register and download the preprint.

Discussion meeting on statistical aspects of the Covid‐19 pandemic
Call for Discussion Paper proposals.

Past discussion meetings

Research Section Online Interactive Discussion Meeting, Wednesday, 13 May 2020 at 4pm

The Discussion paper ‘Linear mixed effects models for non-Gaussian continuous repeated measurement data’ was presented by the authors, Ozgur Asar, David Bolin, Peter J Diggle and Jonas Wallin.

The preprint for the paper is available below and we welcome your contributions in the usual way during the meeting and/or in writing afterwards by 3 June 2020.

We consider the analysis of continuous repeated measurement outcomes that are collected longitudinally. A standard framework for analysing data of this kind is a linear Gaussian mixed effects model within which the outcome variable can be decomposed into fixed effects, time invariant and time-varying random effects, and measurement noise. We develop methodology that, for the first time, allows any combination of these stochastic components to be non-Gaussian, using multivariate normal variance–mean mixtures. To meet the computational challenges that are presented by large data sets, i.e. in the current context, data sets with many subjects and/or many repeated measurements per subject, we propose a novel implementation of maximum likelihood estimation using a computationally efficient subsampling-based stochastic gradient algorithm. We obtain standard error estimates by inverting the observed Fisher information matrix and obtain the predictive distributions for the random effects in both filtering (conditioning on past and current data) and smoothing (conditioning on all data) contexts. To implement these procedures, we introduce an R package: ngme. We reanalyse two data sets, from cystic fibrosis and nephrology research, that were previously analysed by using Gaussian linear mixed effects models.

To be published in Series C; for more information go to the Wiley Online Library

The preprint is available to download.
Linear mixed effects models for non-Gaussian continuous repeated measurement data’ (PDF)