More results on finite sample minimax regret rules

• Econometrics

Date and Location

Wednesday, November 8, 2023, 3:40 PM - 5:00 PM
Blue Room, 1113 Social Sciences and Humanities

Abstract

We study finite sample minimax regret rules when there are T treatments (where T is allowed to be bigger than 2) and the notion of regret is based on the shortfall in expected outcome. In the case, where each treatment has the same number of observations in the sample of size N, we provide a characterization for arbitrary N and T for an unrestricted action space by nature. For other sampling designs, like random sampling, we need to restrict the action space of nature by suitable restrictions.
In the second part of the presentation, we focus attention on finite sample minimax regret rules when the notion of regret is based on the shortfall in the alpha-quantile of the outcome, e.g. the shortfall in median outcome.
The talk is based on joint work with Haoning Chen, Nihal Mehta, and Nikita Pavlov.

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