Lower bound in regression for functional data by representation of small ball probabilities

DOI10.1214/12-EJS726zbMath1295.62043MaRDI QIDQ1950878

André Mas

Publication date: 28 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1348753351

zbMATH Keywords

regular variationlower boundnonparametric regressionfunctional dataGaussian random elementssmall ball problems

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Minimax procedures in statistical decision theory (62C20) Extreme value theory; extremal stochastic processes (60G70) Large deviations (60F10)

Related Items

Adaptive estimation in the functional nonparametric regression model, Nonparametric density estimation for intentionally corrupted functional data, Optimal classification and nonparametric regression for functional data, Minimax regression estimation for Poisson coprocess, Nonparametric modelling for functional data: selected survey and tracks for future, Sobolev regularity of Gaussian random fields, Lasso in Infinite dimension: application to variable selection in functional multivariate linear regression, Nonparametric depth and quantile regression for functional data, Adaptive and minimax estimation of the cumulative distribution function given a functional covariate, Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff, High-dimensional principal projections, Convergence rates for kernel regression in infinite-dimensional spaces, Nonparametric Estimation of Variance Function for Functional Data Under Mixing Conditions, Adaptive regression with Brownian path covariate, Unnamed Item, Uniform in bandwidth consistency for various kernel estimators involving functional data

Uses Software

• fda (R)

Cites Work

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