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 Discussion Meeting (rescheduled from April 2020)
The meeting planned for April 2020 has been postponed and a rescheduled date will be confirmed in due course.
'Quasi-stationary Monte Carlo methods and the ScaLE algorithm'; by Murray Pollock, Paul Fearnhead, Adam M Johansen and Gareth O Roberts.
The preprint for this paper will be available soon.
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)