Functional sufficient dimension reduction through average Fréchet derivatives

DOI10.1214/21-AOS2131zbMath1486.62115OpenAlexW4226400650MaRDI QIDQ2131260

Lexin Li, Kuang-Yao Lee

Publication date: 25 April 2022

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

Full work available at URL: https://doi.org/10.1214/21-aos2131

zbMATH Keywords

consistencyunbiasednessreproducing kernel Hilbert spacefunction-on-function regressionexhaustivenessfunctional central mean subspacefunctional central subspace

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20) Functional data analysis (62R10)

Related Items (2)

Online sparse sliced inverse regression for high-dimensional streaming data ⋮ Sliced Inverse Regression in Metric Spaces

Uses Software

• fda (R)

Cites Work

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• Efficient estimation in sufficient dimension reduction
• Functional linear regression analysis for longitudinal data
• A constructive approach to the estimation of dimension reduction directions
• Sparse Sliced Inverse Regression Via Lasso
• Functional contour regression
• Sufficient dimension reduction based on an ensemble of minimum average variance estimators
• Sampled forms of functional PCA in reproducing kernel Hilbert spaces
• Successive direction extraction for estimating the central subspace in a multiple-index regres\-sion
• An RKHS formulation of the inverse regression dimension-reduction problem
• The hybrid method of FSIR and FSAVE for functional effective dimension reduction
• Optimal prediction for additive function-on-function regression
• Dimension reduction for conditional mean in regression
• Nearest neighbor inverse regression
• Nonlinear sufficient dimension reduction for functional data
• Inverse regression for longitudinal data
• Kernel dimension reduction in regression
• Functional data analysis.
• Contour regression: a general approach to dimension reduction
• On the optimality of sliced inverse regression in high dimensions
• On a Projective Resampling Method for Dimension Reduction With Multivariate Responses
• Optimal Penalized Function-on-Function Regression Under a Reproducing Kernel Hilbert Space Framework
• Additive Function-on-Function Regression
• Interaction Model and Model Selection for Function-on-Function Regression
• Investigating Smooth Multiple Regression by the Method of Average Derivatives
• Support Vector Machines
• On Directional Regression for Dimension Reduction
• Sliced Inverse Regression for Dimension Reduction
• Functional sliced inverse regression analysis
• An Adaptive Estimation of Dimension Reduction Space
• A Semiparametric Approach to Dimension Reduction
• Linear operator‐based statistical analysis: A useful paradigm for big data
• Gradient-Based Kernel Dimension Reduction for Regression
• Effective dimension reduction for sparse functional data
• Functional Additive Models
• Comment

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