Sampling and empirical risk minimization

DOI10.1080/02331888.2016.1259810zbMath1370.62002OpenAlexW2565225715MaRDI QIDQ5276167

Emilie Chautru, Patrice Bertail, Stéphan Clémençon

Publication date: 14 July 2017

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2016.1259810

zbMATH Keywords

empirical processsurvey samplinggeneralization boundempirical risk minimizationHorvitz-Thompson estimationPoisson designHajek

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Sampling theory, sample surveys (62D05)

Related Items (1)

Statistical learning from biased training samples

Cites Work

• Learning from dependent observations
• Large sample theory of empirical distributions in biased sampling models
• Rate of convergence to normal distribution for the Horvitz-Thompson estimator.
• Weighted likelihood estimation under two-phase sampling
• Local Rademacher complexities and oracle inequalities in risk minimization. (2004 IMS Medallion Lecture). (With discussions and rejoinder)
• Empirical Processes in Survey Sampling with (Conditional) Poisson Designs
• The Generalization Ability of Online Algorithms for Dependent Data
• Theory of Classification: a Survey of Some Recent Advances
• A Z‐theorem with Estimated Nuisance Parameters and Correction Note for ‘Weighted Likelihood for Semiparametric Models and Two‐phase Stratified Samples, with Application to Cox Regression’
• On the Convergence of the Horvitz-Thompson Estimator
• Concept learning using complexity regularization
• Asymptotic Theory of Rejective Sampling with Varying Probabilities from a Finite Population
• Convexity, Classification, and Risk Bounds

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