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480 Targeted learningvan der Laan, M. and Dudoit, S. (2003). Unified Cross-validation Methodologyfor Selection Among Estimators and a General Cross-validated AdaptiveEpsilon-net Estimator: Finite Sample Oracle Inequalities and Examples.Technical Report 130, Division of Biostatistics, University of California,Berkeley, CA.van der Laan, M., Dudoit, S., and van der Vaart, A. (2006). The crossvalidatedadaptive epsilon-net estimator. Statistics & Decisions, 24:373–395.van der Laan, M., Polley, E., and Hubbard, A.E. (2007). Super learner. StatisticalApplications in Genetics and Molecular Biology, 6:Article 25.van der Laan, M. and Robins, J.M. (2003). Unified Methods for CensoredLongitudinal Data and Causality. Springer,NewYork.van der Laan, M. and Rose, S. (2010). Statistics ready for a revolution: Nextgeneration of statisticians must build tools for massive data sets. AmstatNews, 399:38–39.van der Laan, M. and Rose, S. (2012). Targeted Learning: Causal Inferencefor Observational and Experimental Data. Springer,NewYork.van der Laan, M. and Rubin, D.B. (2006). Targeted maximum likelihoodlearning. International Journal of Biostatistics, 2:Article 11.van der Vaart, A., Dudoit, S., and van der Laan, M. (2006). Oracle inequalitiesfor multi-fold cross-validation. Statistics & Decisions, 24:351–371.van der Vaart, A. and Wellner, J.A. (1996). Weak Convergence and EmpiricalProcesses. Springer,NewYork.

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