- (230419)Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits
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발표자: Jack R. Porter(University of Wisconsin-Madison)
주제: Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits
We develop asymptotic approximation results that can be applied to sequential
estimation and inference problems, adaptive randomized controlled trials, and other
statistical decision problems that involve multiple decision nodes with structured and
possibly endogenous information sets. Our results extend the classic asymptotic
representation theorem used extensively in efficiency bound theory and local power
analysis. In adaptive settings where the decision at one stage can affect the observation of
variables in later stages, we show that a limiting data environment characterizes all limit
distributions attainable through a joint choice of an adaptive design rule and statistics
applied to the adaptively generated data, under local alternatives. We illustrate how the
theory can be applied to study the choice of adaptive rules and end-of-sample statistical
inference in batched (groupwise) sequential adaptive experiments.
장소: 대우관 본관 3층 323호
날짜: 4월 19일(수) 오전 10시
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