TY  - JOUR
AB  - © 2018 Board of the Foundation of the Scandinavian Journal of Statistics 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 nonregular inference is involved, we show that, under assumptions adopted in this paper, the proposed smoothed Q-learning estimator is asymptotically normally distributed even when the Q-learning estimator is not and its asymptotic variance can be consistently estimated. As a result, inference based on the smoothed Q-learning estimator is standard. We derive the optimal smoothing parameter and propose a data-driven method for estimating it. The finite sample properties of the smoothed Q-learning estimator are studied and compared with several existing estimators including the Q-learning estimator via an extensive simulation study. We illustrate the new method by analyzing data from the Clinical Antipsychotic Trials of Intervention Effectiveness?Alzheimer's Disease (CATIE-AD) study.
AU  - Fan, Y
AU  - He, M
AU  - Su, L
AU  - Zhou, XH
DA  - 2019/06/01
DO  - 10.1111/sjos.12359
EP  - 469
JO  - Scandinavian Journal of Statistics
PY  - 2019/06/01
SP  - 446
TI  - A smoothed Q-learning algorithm for estimating optimal dynamic treatment regimes
VL  - 46
Y1  - 2019/06/01
Y2  - 2026/07/17
ER  -