Robust penalized estimators for functional linear regression

DOI10.1016/j.jmva.2022.105104OpenAlexW3196247073MaRDI QIDQ2111064

Ioannis Kalogridis, Stefan Van Aelst

Publication date: 23 December 2022

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1908.08760

zbMATH Keywords

robustnessasymptoticsregularizationfunctional data

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Functional data analysis (62R10) Nonparametric robustness (62G35) Multivariate analysis (62Hxx)

Uses Software

• fda (R)
• robustbase
• SemiPar
• chemometrics

Cites Work

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• High breakdown-point and high efficiency robust estimates for regression
• A statistical perspective on ill-posed inverse problems (with discussion)
• A reproducing kernel Hilbert space approach to functional linear regression
• Robust estimation for semi-functional linear regression models
• \(M\)-type penalized splines with auxiliary scale estimation
• Functional linear regression that's interpretable
• Methodology and convergence rates for functional linear regression
• Smoothing splines estimators for functional linear regression
• Maximum penalized likelihood estimation. Volume II: Regression
• When the data are functions
• Convergence rate of sieve estimates
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Estimator selection and combination in scalar-on-function regression
• Robust and sparse estimators for linear regression models
• Robust functional linear regression based on splines
• Functional linear model
• M-estimation using penalties or sieves
• Robust elastic net estimators for variable selection and identification of proteomic biomarkers
• Fast robust estimation of prediction error based on resampling
• On rates of convergence in functional linear regression
• Functional data analysis.
• Nonparametric functional data analysis. Theory and practice.
• Functional Principal Component Regression and Functional Partial Least Squares
• High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale
• Semiparametric Regression
• Robust Statistics
• M-estimation for functional linear regression
• A practical guide to splines.
• Methods for Scalar‐on‐Function Regression

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