Interactive -Learning for Quantiles
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DOI: 10.1080/01621459.2016.1155993
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- Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2012. "A Robust Method for Estimating Optimal Treatment Regimes," Biometrics, The International Biometric Society, vol. 68(4), pages 1010-1018, December.
- van der Laan Mark J. & Petersen Maya L, 2007. "Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules," The International Journal of Biostatistics, De Gruyter, vol. 3(1), pages 1-55, March.
- S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
- Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2013. "Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions," Biometrika, Biometrika Trust, vol. 100(3), pages 681-694.
- Chaeryon Kang & Holly Janes & Ying Huang, 2014. "Combining biomarkers to optimize patient treatment recommendations," Biometrics, The International Biometric Society, vol. 70(3), pages 695-707, September.
- Ying-Qi Zhao & Donglin Zeng & Eric B. Laber & Michael R. Kosorok, 2015. "New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 583-598, June.
- Eric B. Laber & Daniel J. Lizotte & Bradley Ferguson, 2014. "Set-valued dynamic treatment regimes for competing outcomes," Biometrics, The International Biometric Society, vol. 70(1), pages 53-61, March.
- van der Laan Mark J. & Petersen Maya L & Joffe Marshall M, 2005. "History-Adjusted Marginal Structural Models and Statically-Optimal Dynamic Treatment Regimens," The International Journal of Biostatistics, De Gruyter, vol. 1(1), pages 1-41, November.
- Erica E. M. Moodie & Thomas S. Richardson, 2010. "Estimating Optimal Dynamic Regimes: Correcting Bias under the Null," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 126-146, March.
- Yingqi Zhao & Donglin Zeng & A. John Rush & Michael R. Kosorok, 2012. "Estimating Individualized Treatment Rules Using Outcome Weighted Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1106-1118, September.
- P. W. Lavori & R. Dawson, 2000. "A design for testing clinical strategies: biased adaptive within‐subject randomization," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(1), pages 29-38.
- Petersen Maya & Schwab Joshua & Gruber Susan & Blaser Nello & Schomaker Michael & van der Laan Mark, 2014. "Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models," Journal of Causal Inference, De Gruyter, vol. 2(2), pages 147-185, September.
- Chaeryon Kang & Holly Janes & Ying Huang, 2014. "Rejoinder: Combining biomarkers to optimize patient treatment recommendations," Biometrics, The International Biometric Society, vol. 70(3), pages 719-720, September.
- Eric B. Laber & Kristin A. Linn & Leonard A. Stefanski, 2014. "Interactive model building for Q-learning," Biometrika, Biometrika Trust, vol. 101(4), pages 831-847.
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