Asymptotic equivalence of functional linear regression and a white noise inverse problem

DOI10.1214/10-AOS872zbMath1221.62011arXiv1211.4742OpenAlexW2057469508MaRDI QIDQ638799

Alexander Meister

Publication date: 14 September 2011

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

Full work available at URL: https://arxiv.org/abs/1211.4742

zbMATH Keywords

functional data analysisnonparametric statisticsstatistical inverse problemswhite noise modelLe Cam equivalence

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Theory of statistical experiments (62B15) Inference from stochastic processes (62M99)

Related Items (20)

Asymptotic equivalence of discretely observed diffusion processes and their Euler scheme: small variance case ⋮ Optimal classification and nonparametric regression for functional data ⋮ Optimal eigen expansions and uniform bounds ⋮ Minimax adaptive tests for the functional linear model ⋮ On the effect of noisy measurements of the regressor in functional linear models ⋮ PCA-based estimation for functional linear regression with functional responses ⋮ Gaussianization machines for non-Gaussian function estimation models ⋮ Asymptotic equivalence for pure jump Lévy processes with unknown Lévy density and Gaussian white noise ⋮ Statistical inference for function-on-function linear regression ⋮ Nonparametric estimation of the ability density in the mixed-effect Rasch model ⋮ On rate optimal local estimation in functional linear regression ⋮ Le cam theory on the comparison of statistical models ⋮ Strong Gaussian approximation of the mixture Rasch model ⋮ Asymptotic equivalence for inhomogeneous jump diffusion processes and white noise ⋮ Asymptotic equivalence of nonparametric diffusion and Euler scheme experiments ⋮ Network representation using graph root distributions ⋮ Adaptive Global Testing for Functional Linear Models ⋮ Adaptive estimation in the linear random coefficients model when regressors have limited variation ⋮ Asymptotic equivalence for nonparametric regression with dependent errors: Gauss-Markov processes ⋮ Relative perturbation bounds with applications to empirical covariance operators

Uses Software

• fda (R)

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