A smoothed Q-learning algorithm for estimating optimal dynamic treatment regime

• المؤلفون الرئيسيون: FAN, Yanqin, HE, Ming, SU, Liangjun, ZHOU, Xiao-Hua
• التنسيق: text
• اللغة: English
• منشور في: Institutional Knowledge at Singapore Management University 2019
• الموضوعات: Asymptotic normality; Exceptional law; Optimal smoothing parameter; Sequential randomization; Wald-type inference; Econometrics
• الوصول للمادة أونلاين: https://ink.library.smu.edu.sg/soe_research/2044; https://ink.library.smu.edu.sg/context/soe_research/article/3043/viewcontent/Smoothed_Q_learning_algorithm_for_estimating_optimal_2016_pp.pdf

In this paper we propose a smoothed Q-learning algorithm for estimating optimal dynamic treatment regimes. In contrast to the Q-learning algorithm in which non-regular inference is involved, we show that under assumptions adopted in this paper, the proposed smoothed Q-learning estimator is asymptoti...