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 online interactive Discussion meeting

Assumption-lean inference for generalised linear model parameters
Tuesday 6 July 2021, 2-4pm (BST)
Register here

DeMO (pre-meeting) with the authors
Tuesday 6 July 2021, 12.30-1.30pm (BST)
Register here

The Discussion Paper 'Assumption-lean inference for generalised linear model parameters' will be presented by the authors, Stijn Vansteelandt and Oliver Dukes, Ghent University, Belgium. 

Chaired by Guy Nason.
 
The preprint for the paper is available to download and we welcome your contributions in the usual way during the meeting and/or in writing afterwards by 20 July 2021.
 
Abstract
Inference for the parameters indexing generalised linear models is routinely based on the assumption that the model is correct and a priori specified. This is unsatisfactory because the chosen model is usually the result of a data-adaptive model selection process, which may induce excess uncertainty that is not usually acknowledged. Moreover, the assumptions encoded in the chosen model rarely represent some a priori known, ground truth, making standard inferences prone to bias, but also failing to give a pure reflection of the information that is contained in the data. Inspired by developments on assumption-free inference for so-called projection parameters, we here propose novel nonparametric definitions of main effect estimands and effect modification estimands. These reduce to standard main effect and effect modification parameters in generalised linear models when these models are correctly specified, but have the advantage that they continue to capture respectively the (conditional) association between two variables, or the degree to which two variables interact in their association with outcome, even when these models are misspecified. We achieve an assumption-lean inference for these estimands on the basis of their efficient influence function under the nonparametric model while invoking flexible data-adaptive (eg machine learning) procedures.
 
To be published in Series B; for more information go to the Wiley Online Library.


Past Discussion meetings

View our playlist of recent Discussion Meetings
Read past Discussion Papers

Gaussian differential privacy
16 December 2020

The Discussion paper ‘Gaussian Differential Privacy’ by Jinshuo Dong, Aaron Roth, and Weijie J Su was presented by Weijie Su and Jinshuo Dong of the University of Pennsylvania, Philadelphia, USA.

The meeting was preceded (on the same day) by an introductory pre-meeting (DeMO) at 3:30-4:30pm with presenter Jinshuo Dong.

To be published in Series B; for more information go to the Wiley Online Library.
Download the preprint (PDF) - but please note the deadline for contributions has passed.

Watch the meeting (YouTube)
Watch the DeMO (YouTube)