[63] William A Massey, Geraldine A Parker, and Ward Whitt. Estimating the parameters of a nonhomogeneous poisson process with linear rate. Telecommunication systems, 5:361-388, 1996.
[64] Alberto Maria Metelli, Francesco Trovo, Matteo Pirola, and Marcello Restelli. Stochastic rising bandits. In International Conference on Machine Learning, pages 15421-15457. PMLR, 2022.
[65] Seungki Min and Daniel Russo. An information-theoretic analysis of nonstationary bandit learning. arXiv preprint arXiv:2302.04452, 2023.
[66] Yonatan Mintz, Anil Aswani, Philip Kaminsky, Elena Flowers, and Yoshimi Fukuoka. Nonstationary bandits with habituation and recovery dynamics. Operations Research, 68(5):1493-1516, 2020.
[67] Xinkun Nie, Xiaoying Tian, Jonathan Taylor, and James Zou. Why adaptively collected data have negative bias and how to correct for it. In International Conference on Artificial Intelligence and Statistics, pages 1261–1269. PMLR, 2018.
[68] Tobias Nilsson and Gunnar Elgered. Long-term trends in the atmospheric water vapor content estimated from ground-based gps data. Journal of Geophysical Research: Atmospheres, 113(D19), 2008.
[69] Steven Piantadosi. Clinical trials: a methodologic perspective. John Wiley & Sons, 2017.
[70] Chao Qin and Daniel Russo. Adaptivity and confounding in multi-armed bandit experiments. arXiv preprint arXiv:2202.09036, 2022.
[71] Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, and Michal Valko. Rotting bandits are no harder than stochastic ones. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 2564-2572. PMLR, 2019.
[72] Julien Seznec, Pierre Menard, Alessandro Lazaric, and Michal Valko. A single algorithm for both restless and rested rotting bandits. In International Conference on Artificial Intelligence and Statistics, pages 3784-3794. PMLR, 2020.
[73] David Simchi-Levi and Chonghuan Wang. Multi-armed bandit experimental design: Online decision-making and adaptive inference. Available at SSRN 4224969, 2022.
[74] David Simchi-Levi and Chonghuan Wang. Multi-armed bandit experimental design: Online decision-making and adaptive inference. In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, pages 3086-3097. PMLR, 25-27 Apr 2023.
[75] Richard Simon. Adaptive treatment assignment methods and clinical trials. Biometrics, pages 743-749, 1977.
[76] Adith Swaminathan and Thorsten Joachims. Batch learning from logged bandit feedback through counterfactual risk minimization. The Journal of Machine Learning Research, 16(1):1731-1755, 2015.
[77] William R Thompson. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, 25(3/4):285-294, 1933.
[78] Stefan Wager and Kuang Xu. Diffusion asymptotics for sequential experiments. arXiv preprint arXiv:2101.09855, 2021.
[79] Martin J Wainwright. High-dimensional statistics: A non-asymptotic viewpoint, volume 48. Cambridge University Press, 2019.
[80] Jing Wang, Peng Zhao, and Zhi-Hua Zhou. Revisiting weighted strategy for non-stationary parametric bandits. arXiv preprint arXiv:2303.02691, 2023.
[81] Siwei Wang, Longbo Huang, and John Lui. Restless-ucb, an efficient and low-complexity algorithm for online restless bandits. Advances in Neural Information Processing Systems, 33:11878–11889, 2020.