Functional Principal Component Regression and Functional Partial Least‐squares Regression: An Overview and a Comparative Study
From MaRDI portal
Publication:6064648
DOI10.1111/insr.12116OpenAlexW2103659910MaRDI QIDQ6064648
Wenceslao González Manteiga, Pedro Galeano, Manuel Febrero-Bande
Publication date: 10 November 2023
Published in: International Statistical Review (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/insr.12116
eigenvalueseigenfunctionscross-validationfunctional linear modelfunctional principal componentsfunctional partial least squares
Related Items (20)
Estimation, imputation and prediction for the functional linear model with scalar response with responses missing at random ⋮ Robust functional principal components for sparse longitudinal data ⋮ On dimension reduction models for functional data ⋮ Function-on-function partial quantile regression ⋮ Estimation of a functional single index model with dependent errors and unknown error density ⋮ Sparse semiparametric regression when predictors are mixture of functional and high-dimensional variables ⋮ Methods for Scalar‐on‐Function Regression ⋮ Factor-augmented Model for Functional Data ⋮ Covariance estimation error of incomplete functional data under RKHS framework ⋮ Recent advances in functional data analysis and high-dimensional statistics ⋮ High-dimensional functional time series forecasting: an application to age-specific mortality rates ⋮ Modelling Functional Data with High-dimensional Error Structure ⋮ A robust partial least squares approach for function-on-function regression ⋮ Dynamical multiple regression in function spaces, under kernel regressors, with ARH(1) errors ⋮ Functional continuum regression ⋮ Robust functional regression based on principal components ⋮ A partial least squares approach for function-on-function interaction regression ⋮ Sparse principal component regression via singular value decomposition approach ⋮ On functional data analysis and related topics ⋮ Resolvent estimators for functional autoregressive processes with random coefficients
Cites Work
- Unnamed Item
- Unnamed Item
- The Degrees of Freedom of Partial Least Squares Regression
- Functional linear regression analysis for longitudinal data
- Functional data analysis with increasing number of projections
- Inference for functional data with applications
- Detecting changes in functional linear models
- Methodology and theory for partial least squares applied to functional data
- An introduction to functional data analysis and a principal component approach for testing the equality of mean curves
- Functional linear regression that's interpretable
- Prediction in functional linear regression
- CLT in functional linear regression models
- PLS regression on a stochastic process
- Methodology and convergence rates for functional linear regression
- Smoothing splines estimators for functional linear regression
- Diagnostics for functional regression via residual processes
- Measures of influence for the functional linear model with scalar response
- Thresholding projection estimators in functional linear models
- Applied functional data analysis. Methods and case studies
- Functional linear model
- Generalized functional linear models
- Generalized additive models for functional data
- A test of significance in functional quadratic regression
- On rates of convergence in functional linear regression
- Nonparametric functional data analysis. Theory and practice.
- Presmoothing in functional linear regression
- Functional Principal Component Regression and Functional Partial Least Squares
- Testing for No Effect in Functional Linear Regression Models, Some Computational Approaches
- Testing Hypotheses in the Functional Linear Model
- Generalized Linear Models with Functional Predictors
- Functional quadratic regression
- On Properties of Functional Principal Components Analysis
This page was built for publication: Functional Principal Component Regression and Functional Partial Least‐squares Regression: An Overview and a Comparative Study