Department seminar by Adam Kashlak, University of AlbertaExport this event to calendar

Thursday, November 14, 2019 — 4:00 PM EST

On Khintchine's Inequality for Statistics


In complex estimation and hypothesis testing settings, it may be impossible to compute p-values or construct confidence intervals using classical analytic approaches like asymptotic normality.  Instead, one often relies on randomization and resampling procedures such as the bootstrap or permutation test.  But these approaches suffer from the computational burden of large scale Monte Carlo runs.  To remove this burden, we develop analytic methods for hypothesis testing and confidence intervals by specifically considering the discrete finite sample distributions of the randomized test statistic.  The primary tool we use to achieve such results is Khintchine's inequality and its extensions and generalizations.

Location 
M3 - Mathematics 3
Room: 3127
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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