Aggregation using input-output trade-off

DOI10.1016/j.jspi.2018.08.001zbMath1418.62116arXiv1803.03166OpenAlexW2963979365MaRDI QIDQ2317264

Aurélie Fischer, Mathilde Mougeot

Publication date: 9 August 2019

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1803.03166

zbMATH Keywords

classificationaggregationconsistencynonlinearityregression estimation

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric estimation (62G05)

Related Items (3)

Some results on statistics and functional data analysis ⋮ KFC: A clusterwise supervised learning procedure based on the aggregation of distances ⋮ A nearest-neighbor-based ensemble classifier and its large-sample optimality

Uses Software

• COBRA

Cites Work

• Unnamed Item
• Unnamed Item
• COBRA: a combined regression strategy
• A nonlinear aggregation type classifier
• Concentration inequalities and model selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII -- 2003.
• Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity
• An equivalence theorem for \(L_ 1\) convergence of the kernel regression estimate
• Model selection in nonparametric regression
• Aggregating regression procedures to improve performance
• A kernel-based combined classification rule
• Combining different procedures for adaptive regression
• An almost surely optimal combined classification rule
• A distribution-free theory of nonparametric regression
• Functional aggregation for nonparametric regression.
• Statistical learning theory and stochastic optimization. Ecole d'Eté de Probabilitiés de Saint-Flour XXXI -- 2001.
• Aggregated estimators and empirical complexity for least square regression
• Sparsity oracle inequalities for the Lasso
• Aggregation for Gaussian regression
• A COMPARISON STUDY OF SOME COMBINED CLASSIFIERS
• Adaptive Regression by Mixing
• Combining Classifiers via Discretization
• Aggregation and Sparsity Via ℓ1 Penalized Least Squares

This page was built for publication: Aggregation using input-output trade-off