Functional Linear Regression Analysis Based on Partial Least Squares and Its Application
DOI10.1007/978-3-319-40643-5_15zbMath1366.62143OpenAlexW2530921433MaRDI QIDQ5278377
Publication date: 19 July 2017
Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-319-40643-5_15
basis functionfunctional data analysisemergency patientscomputer related techniquecorrelation with responsefunctional principal component (FPC)partial least squares regressions (PLSR)
Factor analysis and principal components; correspondence analysis (62H25) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10)
Uses Software
Cites Work
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- Simultaneous estimation and factor selection in quantile regression via adaptive sup-norm regularization
- Methodology and theory for partial least squares applied to functional data
- A reproducing kernel Hilbert space approach to functional linear regression
- Prediction in functional linear regression
- PLS regression on a stochastic process
- GACV for quantile smoothing splines
- Methodology and convergence rates for functional linear regression
- Smoothing splines estimators for functional linear regression
- Estimating the dimension of a model
- Functional data analysis
- Kernel-based functional principal components
- Applied functional data analysis. Methods and case studies
- Model-based clustering for multivariate functional data
- Principal components for multivariate functional data
- Smoothed functional principal components analysis by choice of norm
- Variable selection for functional regression models via the \(L_1\) regularization
- Covariate adjusted functional principal components analysis for longitudinal data
- Nonparametric functional data analysis. Theory and practice.
- Properties of principal component methods for functional and longitudinal data analysis
- Inference for Density Families Using Functional Principal Component Analysis
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Minimax and Adaptive Prediction for Functional Linear Regression
- Shrinkage estimation and selection for multiple functional regression
- Regularization and Variable Selection Via the Elastic Net
- Model Selection and Estimation in Regression with Grouped Variables
- On Properties of Functional Principal Components Analysis
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