The Cross-Validated Adaptive Epsilon-Net Estimator

By M. J. van der Laan, Sandrine Dudoit, A. van der Vaart. 2006

159 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/af071a2bf35b111027b3c4941e5e34d4c5ff5e36

How researchers see this paper

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

In Computational Theory and Mathematics

1. Peter Bhlmann
2. Michael J. Kane
3. M. J. van der Laan
4. Petros Drineas
5. Susan Athey
6. Stefan Wager
7. Michael W. Mahoney
8. David P. Woodruff

In Statistics and Probability

1. Stefan Wager
2. Susan Athey
3. Mihaela van der Schaar
4. Guido W. Imbens
5. Peter Bhlmann
6. Michael J. Kane
7. Petros Drineas
8. Ahmed M. Alaa

Citations in the corpus

• Targeted Data Adaptive Estimation of the Causal Dose–Response Curve (2013, unknown): “11 of van der Vaart [2002], if Lĝ falls in a Glivenko-Cantelli class {Lg : g ∈ G} with probability tending to one, and P0(Lĝ − Lg0) → 0, then the IPTW estimator is consistent for R0(ψ).”
• CONSISTENCY OF CROSS VALIDATION FOR COMPARING REGRESSION PROCEDURES (2007, background): “, van der Laan and Dudoit [26], van der Laan, Dudoit and van der Vaart [27] and van der Vaart, Dudoit and van der Laan [28]), which can be applied in other contexts (e.”
• The International Journal of Biostatistics (2017, unknown): “…minimax estimators for infinite dimensional parameters of the full data distribution in CAR-censored data and causal inference models: see also van der Laan et al. (2006), van der Vaart et al. (2006), van der Laan et al. (2004), Dudoit and van der Laan (2005), Kele¸s et al. (2002), Sinisi and van…”
• Handbook of Big Data (2016, background): “Then, for every δ > 0, there exists a constant C(δ) <∞ (defined in [13]) such that”
• Statistical Applications in Genetics and Molecular Biology Super Learner (2011, unknown): “…A1 that the loss function L(O,ψ) = (Y − ψ(X))2 is uniformly bounded, and the Assumption A2 that the variance of the ψ0centered loss function L(O,ψ)−L(O,ψ0) can be bounded by its expectation uniformly in ψ, van der Laan et al. (2006) (Theorem 3.1) establish the following finite sample inequality.”
• Policy Learning With Observational Data (2017, background): “The only other available 1 / √ n -bounds for policy learning in observational studies with a binary treatment that we are aware of are a result of van der Laan, Dudoit, and van der Vaart (2006) for the case where Π consists of a finite set of policies whose cardinality grows with n , and a result of ”