Efficient estimation of linear functionals of principal components

DOI10.1214/19-AOS1816zbMath1440.62232arXiv1708.07642MaRDI QIDQ2176629

Richard Nickl, Matthias Löffler, Vladimir I. Koltchinskii

Publication date: 5 May 2020

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

Full work available at URL: https://arxiv.org/abs/1708.07642

zbMATH Keywords

asymptotic normalityprincipal component analysisspectral projectionssemiparametric efficiency

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Asymptotic distribution theory in statistics (62E20) Central limit and other weak theorems (60F05) Approximations to statistical distributions (nonasymptotic) (62E17)

Related Items

Lower bounds for invariant statistical models with applications to principal component analysis, Singular vector and singular subspace distribution for the matrix denoising model, Asymptotically efficient estimation of smooth functionals of covariance operators, Efficient estimation of linear functionals of principal components, Rates of Bootstrap Approximation for Eigenvalues in High-Dimensional PCA, Bootstrapping max statistics in high dimensions: near-parametric rates under weak variance decay and application to functional and multinomial data, Inference for low-rank models, Estimating covariance and precision matrices along subspaces, Efficient estimation of smooth functionals in Gaussian shift models, Estimation of smooth functionals of location parameter in Gaussian and Poincaré random shift models., Estimation of smooth functionals in normal models: bias reduction and asymptotic efficiency, Estimation of smooth functionals in high-dimensional models: bootstrap chains and Gaussian approximation, Bootstrapping the operator norm in high dimensions: error estimation for covariance matrices and sketching

Uses Software

• fda (R)

Cites Work

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