The cross-validated adaptive epsilon-net estimator
From MaRDI portal
Publication:3438354
DOI10.1524/stnd.2006.24.3.373zbMath1111.62003OpenAlexW2018331991MaRDI QIDQ3438354
Aad W. van der Vaart, Sandrine Dudoit, Mark J. Van der Laan
Publication date: 15 May 2007
Published in: Statistics & Decisions (Search for Journal in Brave)
Full work available at URL: https://biostats.bepress.com/ucbbiostat/paper142
maximum likelihood estimationcross-validationloss functioncovering numberadaptationoracle inequalities
Density estimation (62G07) Linear inference, regression (62J99) Foundations and philosophical topics in statistics (62A01)
Related Items
Estimator selection and combination in scalar-on-function regression, Tree-based multivariate regression and density estimation with right-censored data, Aggregated hold out for sparse linear regression with a robust loss function, Discussion of ``Identification, estimation and approximation of risk under interventions that depend on the natural value of treatment using observational data, by Jessica Young, Miguel Hernán, and James Robins, Tree-based censored regression with applications in insurance, Empirical risk minimization in inverse problems, Comparing different propensity score estimation methods for estimating the marginal causal effect through standardization to propensity scores, Orthogonal statistical learning, Causal survival analysis under competing risks using longitudinal modified treatment policies, A simple method for combining estimates to improve the overall error rates in classification, A cross-validation deletion-substitution-addition model selection algorithm: application to marginal structural models, Consistency of cross validation for comparing regression procedures, Cross-validated bagged learning, An Application of Targeted Maximum Likelihood Estimation to the Meta‐Analysis of Safety Data, A survey of cross-validation procedures for model selection, Asymptotics of cross-validated risk estimation in estimator selection and performance assess\-ment, Consistency of empirical Bayes and kernel flow for hierarchical parameter estimation, Improving multilabel classification via heterogeneous ensemble methods, Using Machine Learning Methods to Support Causal Inference in Econometrics