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Bibliographic Details
Main Authors: Hirano, Keisuke, Porter, Jack R.
Format: Preprint
Published: 2023
Subjects:
Online Access:https://arxiv.org/abs/2302.03117
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author Hirano, Keisuke
Porter, Jack R.
author_facet Hirano, Keisuke
Porter, Jack R.
contents We develop asymptotic approximations that can be applied to sequential estimation and inference problems, adaptive randomized controlled trials, and related settings. In batched adaptive settings where the decision at one stage can affect the observation of variables in later stages, our asymptotic representation characterizes all limit distributions attainable through a joint choice of an adaptive design rule and statistics applied to the adaptively generated data. This facilitates local power analysis of tests, comparison of adaptive treatments rules, and other analyses of batchwise sequential statistical decision rules.
format Preprint
id arxiv_https___arxiv_org_abs_2302_03117
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits
Hirano, Keisuke
Porter, Jack R.
Econometrics
We develop asymptotic approximations that can be applied to sequential estimation and inference problems, adaptive randomized controlled trials, and related settings. In batched adaptive settings where the decision at one stage can affect the observation of variables in later stages, our asymptotic representation characterizes all limit distributions attainable through a joint choice of an adaptive design rule and statistics applied to the adaptively generated data. This facilitates local power analysis of tests, comparison of adaptive treatments rules, and other analyses of batchwise sequential statistical decision rules.
title Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits
topic Econometrics
url https://arxiv.org/abs/2302.03117