Regression imputation in the functional linear model with missing values in the response

DOI10.1016/j.jspi.2018.12.004zbMath1418.62208OpenAlexW2779146491MaRDI QIDQ2317298

Yousri Henchiri, Christophe Crambes

Publication date: 9 August 2019

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2018.12.004

zbMATH Keywords

missing datamissing at randomfunctional linear modelprincipal components regressionmean square error prediction

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Factor analysis and principal components; correspondence analysis (62H25) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Applications of statistics to environmental and related topics (62P12)

Related Items (6)

Functional linear model with partially observed covariate and missing values in the response ⋮ Estimation for a hybrid model of functional and linear measurement errors regression with missing response ⋮ On the impact of missing outcomes in linear regression ⋮ Functional regression with dependent error and missing observation in reproducing kernel Hilbert spaces ⋮ Empirical likelihood in single-index partially functional linear model with missing observations ⋮ Extremal behaviour of a periodically controlled sequence with imputed values

Uses Software

• fda (R)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Inference for functional data with applications
• Missing data. Analysis and design
• Prediction in functional linear regression
• Methodology and convergence rates for functional linear regression
• Smoothing splines estimators for functional linear regression
• A practical guide to splines
• Linear processes in function spaces. Theory and applications
• Functional linear model
• Partial identification of probability distributions.
• Nonparametric regression estimation with missing data
• Asymptotics of prediction in functional linear regression with functional outputs
• Functional data analysis.
• Nonparametric functional data analysis. Theory and practice.
• Nonparametric curve estimation with missing data: a general empirical process approach
• Mean estimation with data missing at random for functional covariables
• Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data
• SPECIFICATION TEST FOR MISSING FUNCTIONAL DATA
• Functional Data Analysis with R and MATLAB
• Nonparametric Estimation of Mean Functionals with Data Missing at Random
• Analysis of Variance for Functional Data
• Semiparametric Regression Analysis With Missing Response at Random

This page was built for publication: Regression imputation in the functional linear model with missing values in the response