Gao, Yuhe, Shi, Chengchun ORCID: 0000-0001-7773-2099 and Song, Rui (2023) Deep spectral Q-learning with application to mobile health. Stat, 12 (1). ISSN 2049-1573
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Abstract
Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.
Item Type: | Article |
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Official URL: | https://onlinelibrary.wiley.com/journal/20491573 |
Additional Information: | © 2023 The Authors |
Divisions: | Statistics |
Subjects: | H Social Sciences > HA Statistics |
Date Deposited: | 19 Jun 2023 15:09 |
Last Modified: | 07 Oct 2024 16:03 |
URI: | http://eprints.lse.ac.uk/id/eprint/119445 |
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