<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Karun Adusumilli | Macro Paper Warehouse</title><link>https://macropaperwarehouse.com/authors/karun-adusumilli/</link><description>Karun Adusumilli</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><atom:link href="https://macropaperwarehouse.com/authors/karun-adusumilli/index.xml" rel="self" type="application/rss+xml"/><item><title>Optimal Tests Following Sequential Experiments</title><link>https://macropaperwarehouse.com/papers/optimal-tests-following-sequential-experiments/</link><guid>https://macropaperwarehouse.com/papers/optimal-tests-following-sequential-experiments/</guid><description>&lt;p&gt;This paper addresses a practical gap in the inference literature for sequential and adaptive experiments: while the design of such experiments has been studied extensively, there is little theory characterizing which tests are optimal once the experiment concludes. Adusumilli asks what the best hypothesis test looks like after a sequential experiment — a costly sampling design, a group sequential trial, or a bandit experiment — and whether the complexity of the adaptive protocol can be reduced to a manageable set of sufficient statistics for inference purposes.&lt;/p&gt;</description></item></channel></rss>