Connecting the False Discovery Rate to shrunk estimates

Add to your list(s) Download to your calendar using vCal

• Nick W Galwey (Former Statistics Leader, Research Statistics, at GlaxoSmithKline Research and Development (Retired))
• Tuesday 03 February 2026, 19:15-21:00
• MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF.

If you have a question about this talk, please contact Peter Watson.

Science is currently facing a ‘replication crisis’ – a concern that many scientific findings reported are difficult or impossible to reproduce. A major cause of this is the availability of technology that permits the exploration and testing of very large numbers of hypotheses, some of which will almost certainly show large or significant effects by chance, even when no real effects are present: this is the ‘multiplicity’ or ‘multiple testing’ problem. The tools available to address this problem include: shrunk estimates, which reduce the estimated effect in relation to each hypothesis from the observed value towards the null value, and the False Discovery Rate (FDR), which relates to the subset of the hypotheses tested for which the discovery of an effect is announced, and states the proportion of these ‘discoveries’ that is expected to be false. This talk will first examine the conceptual basis for each of these tools, then consider how they are connected. Though shrunk estimates and the FDR are both conventionally presented in the frequentist statistical framework, they can both also be presented in empirical-Bayesian terms, with the prior distribution being provided by: the distribution of effect sizes over the full set of hypotheses (in the case of shrunk estimates), and the distribution of significance-test p-values over the subset of hypotheses giving ‘discoveries’ (in the case of the FDR ). Based on this connection, a formal relationship between shrunk estimates and FDR values, for a normally-distributed response variable, will be illustrated. The talk will conclude by considering which of the two tools is the more appropriate in different practical circumstances.

This talk is part of the Cambridge Statistics Discussion Group (CSDG) series.

This talk is included in these lists:

• All CMS events
• All Talks (aka the CURE list)
• BHRU Annual Lecture 2015
• BHRU Annual Lecture 2016
• CMS Events
• Cambridge Statistics Discussion Group (CSDG)
• Cardiovascular Epidemiology Unit Special Seminars
• DPMMS Lists
• DPMMS info aggregator
• DPMMS lists
• Department of Public Health and Primary Care
• Hanchen DaDaDash
• Interested Talks
• MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF
• PublicHealth@Cambridge
• School of Physical Sciences
• Statistical Laboratory info aggregator
• bld31

Note that ex-directory lists are not shown.