Estimating the algorithmic variance of randomized ensembles via the bootstrap

DOI10.1214/18-AOS1707zbMath1415.62045arXiv1907.08742MaRDI QIDQ666594

Miles E. Lopes

Publication date: 6 March 2019

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1907.08742

zbMATH Keywords

bootstraprandomized algorithmsrandom forestsbagging

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric statistical resampling methods (62G09) Learning and adaptive systems in artificial intelligence (68T05) Randomized algorithms (68W20) Prediction theory (aspects of stochastic processes) (60G25)

Related Items (9)

Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting ⋮ Randomized numerical linear algebra: Foundations and algorithms ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Estimating the algorithmic variance of randomized ensembles via the bootstrap ⋮ Estimating a sharp convergence bound for randomized ensembles ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Bootstrapping the operator norm in high dimensions: error estimation for covariance matrices and sketching

Uses Software

• AdaBoost.MH
• randomForest
• ElemStatLearn

Cites Work

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