Unified Cross-Validation Methodology For Selection Among Estimators and a General Cross-Validated Adaptive Epsilon-Net Estimator: Finite Sample Oracle Inequalities and Examples

By M. J. van der Laan, Sandrine Dudoit. 2003

283 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8e3cfaa13b97d152833b429b179eed389394eecf

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Citations in the corpus

The International Journal of Biostatistics Statistical Inference for Variable Importance (2011, methodology): “Because of this issue, we like to also point out that Result 3 allows us to apply the unified loss based estimation methodology in van der Laan and Dudoit (2003) to the parameter Ψ(P )(a, V ) = EP (EP (Y | A = a,W )−E(Y | A = 0,W ) | V ) by defining the loss function for ψ as L(O,ψ | Π, θ) ≡ (D(O | ”
Sequential Double Robustness in Right-Censored Longitudinal Models (2017, methodology): “We also briefly discuss the augmented IPW (AIPW) version of this loss function (van der Laan and Dudoit, 2003; Keles et al., 2004) and its double robustness property.”
Choice between Semi‐parametric Estimators of Markov and Non‐Markov Multi‐state Models from Coarsened Observations (2007, methodology): “The problem of choice of an estimator among a family of estimators using the expected Kullback–Leibler risk has been studied in particular by Hall (1987), van der Laan & Dudoit (2003) and van der Laan et al. (2004).”
The International Journal of Biostatistics (2017, unknown): “Under the assumption that L 1 ( Q ) is uniformly bounded in all candidate Q ’s, we can apply the Theorems on the cross-validation selector (e.g. van der Laan and Dudoit (2003)), which proves that either the cross-validation selector is asymptotically equivalent with the oracle selector, or it…”
Stacked Generalization: An Introduction to Super Learning (2017, unknown): “613Ŷearth (3) where ŶSL denotes our final Super Learner predicted outcome.”
Extending Marginal Structural Models through Local, Penalized, and Additive Learning (2006, unknown): “Rather than constructing a single empirical risk minimizer based on a candidate set Ψ, van der Laan and Dudoit (2003) considered the sieve-based approach of nesting candidate sets Ψ1 ⊂ Ψ2 ⊂ .”
Tree-based multivariate regression and density estimation with right-censored data (2004, unknown): “…62H99; 62N02; 62G99; 62G05; 62G07; 62G08; 62C99; 62P10 Keywords: CART; Censored data; Comparative genomic hybridization; Cross-validation; Density estimation; Loss function; Microarray; Model selection; Multivariate outcome; Prediction; Regression tree; Risk estimation; Survival analysis”
Targeted Data Adaptive Estimation of the Causal Dose–Response Curve (2013, unknown): “An analogue version of this theorem for the CV-A-IPTW is presented in van der Laan and Dudoit [2003]. The CV-TMLE is expected to perform better than the CV-A-IPTW in finite sample sizes, in which practical positivity violations are often present and lead to CV-A-IPTW estimators that are either very ”
Handbook of Big Data (2016, methodology): “The targeted learning framework for efficient estimation was introduced nearly a decade ago [62], following key advances where efficient influence curves were used as estimating functions for effect estimation [27–30,59], and unified loss-based machine learning methods were developed for fitting inf”
Statistical Applications in Genetics and Molecular Biology Super Learner (2011, unknown): “Theoretical results show that such a super learner will perform asymptotically as well as or better than any of the candidate learners (van der Laan and Dudoit, 2003; van der Laan et al., 2006).”
Towards optimal doubly robust estimation of heterogeneous causal effects (2020, methodology): “Several important oracle inequalities for cross-validated selection of estimators exist in the literature [Díaz et al., 2018, Luedtke and van der Laan, 2016, van der Laan and Dudoit, 2003].”