Functional principal subspace sampling for large scale functional data analysis

DOI10.1214/22-EJS2010zbMath1493.62642arXiv2109.03397MaRDI QIDQ2137809

Xiaomeng Yan, Shiyuan He

Publication date: 11 May 2022

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

Full work available at URL: https://arxiv.org/abs/2109.03397

zbMATH Keywords

randomized algorithmfunctional linear regressionfunctional principal component analysisoperator perturbation theory

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Functional data analysis (62R10) Statistical sampling theory and related topics (62D99)

Related Items (1)

Optimal subsampling for functional quantile regression

Uses Software

• OSUMC

Cites Work

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• Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
• Functional linear regression analysis for longitudinal data
• Inference for functional data with applications
• Asymptotics and concentration bounds for bilinear forms of spectral projectors of sample covariance
• Kernel ridge vs. principal component regression: minimax bounds and the qualification of regularization operators
• A reproducing kernel Hilbert space approach to functional linear regression
• Faster least squares approximation
• Estimation in functional linear quantile regression
• CLT in functional linear regression models
• Methodology and convergence rates for functional linear regression
• Properties of design-based functional principal components analysis
• Uniform convergence and asymptotic confidence bands for model-assisted estimators of the mean of sampled functional data
• Randomized sketches for kernels: fast and optimal nonparametric regression
• Defining probability density for a distribution of random functions
• On some extensions of Bernstein's inequality for self-adjoint operators
• Confidence bands for Horvitz-Thompson estimators using sampled noisy functional data
• Computational Advertising: Techniques for Targeting Relevant Ads
• Newton Sketch: A Near Linear-Time Optimization Algorithm with Linear-Quadratic Convergence
• Randomized Sketches of Convex Programs With Sharp Guarantees
• Horvitz-Thompson estimators for functional data: asymptotic confidence bands and optimal allocation for stratified sampling
• Principal component models for sparse functional data
• Introduction to Functional Data Analysis
• Optimal Subsampling for Large Sample Logistic Regression
• Optimal Sampling for Generalized Linear Models Under Measurement Constraints
• Estimation of the Mean of Functional Time Series and a Two-Sample Problem
• Rotation sampling for functional data
• Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
• Sketching for Principal Component Regression
• Subspace Sampling and Relative-Error Matrix Approximation: Column-Row-Based Methods
• Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication
• Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix
• Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition
• An Introduction to Matrix Concentration Inequalities
• Functional Data Analysis for Sparse Longitudinal Data
• OSUMC

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