Inference for Ranks

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  • Date and time: Wednesday 23 February 2022, 1pm to 2pm
  • Location: Online only
  • Audience: Open to All
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Event details

This seminar is hosted by Weining Wang.

It is often desired to rank different populations according to the value of some feature of each population. For example, it may be desired to rank neighbourhoods according to some measure of intergenerational mobility or countries according to some measure of academic achievement. These rankings are invariably computed using estimates rather than the true values of these features. As a result, there may be considerable uncertainty concerning the rank of each population. In this paper, we consider the problem of accounting for such uncertainty by constructing confidence sets for the rank of each population.

We consider both the problem of constructing marginal confidence sets for the rank of a particular population as well as simultaneous confidence sets for the ranks of all populations. We show how to construct such confidence sets under weak assumptions. An important feature of all of our constructions is that they remain computationally feasible even when the number of populations is very large. We apply our theoretical results to re-examine the rankings of both neighbourhoods in the United States in terms of intergenerational mobility and developed countries in terms of academic achievement.

The conclusions about which countries do best and worst at reading, math, and science are fairly robust to accounting for uncertainty. The confidence sets for the ranking of the 50 most populous commuting zones by mobility are also found to be small. However, the mobility rankings become much less informative if one includes all commuting zones, if one considers neighbourhoods at a more granular level (counties, Census tracts), or if one uses movers across areas to address concerns about selection.

About the speaker

Daniel Wilhelm (University College London)