Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m‐Out‐of‐n Bootstrap Scheme

By Bibhas Chakraborty, Eric B. Laber, Ying‐Qi Zhao. 2013

131 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

In Statistics and Probability

Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “rules as well as nonregularity of the parameter estimations in traditional RL methods by proposing various extensions over default Q-learning procedure in order to increase the robustness of learning [167]. Laber et al. [168] proposed a new version of Q-learning, interactive Qlearning (IQ-learning),”
Estimation of Optimal Dynamic Treatment Regimes (2014, background): “Coefficients indexing the stage1 Q-function are statistically non-regular [5,23,56].”
Q-Learning: Theory and Applications (2020, background): “…discussion of the connections between the The max operator in Equation 8 causes difficulties for statistical inference that have been the focus of much research on Q-learning since its introduction to statistics (Chakraborty et al. 2010, 2013; Moodie et al.2010; Laber et al.2014b; Song et al.2015).”
Penalized Q-Learning for Dynamic Treatment Regimens (2015, unknown): “Using individual selection enjoys similar shrinkage advantages as do penalized methods described by Frank and Friedman (1993), Tibshirani (1996), Fan and Li (2001), Candes and Tao (2007), Zou (2006) and Zou and Li (2008).”
Q-learning Residual Analysis: Application to The Effectiveness of Sequences of Antipsychotic Medications for Patients with Schizophrenia (2016, methodology): “For more information, see Appendix A of the supplementary material and [22, 36], [30], [37], and [38].”
Dynamic treatment regimes: technical challenges and applications (2014, methodology): “…with the following methods: the centered percentile bootstrap (CPB); the centered percentile bootstrap of the soft-thresholding (ST) method of Chakraborty et al. (2009) as described in Section 3; and the adaptive m-out-of-n (MOFN) bootstrap with data-driven tuning of Chakraborty et al. (2013).”