Functional linear model with partially observed covariate and missing values in the response
DOI10.1080/10485252.2022.2142222OpenAlexW4281618569MaRDI QIDQ5881431
Unnamed Author, Christophe Crambes, Ali Gannoun, Yousri Henchiri
Publication date: 10 March 2023
Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10485252.2022.2142222
missing datamissing at randommissing completely at randomfunctional linear modelregression imputationfunctional principal components
Asymptotic properties of parametric estimators (62F12) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Applications of statistics (62P99)
Cites Work
- 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
- Estimation, imputation and prediction for the functional linear model with scalar response with responses missing at random
- Semi-functional partially linear regression model with responses missing at random
- Functional linear model
- On the optimal reconstruction of partially observed functional data
- Ridge reconstruction of partially observed functional data is asymptotically optimal
- Estimation for functional partial linear models with missing responses
- Regression imputation in the functional linear model with missing values in the response
- Nonparametric regression estimation for functional stationary ergodic data with missing at random
- Functional data analysis.
- Nonparametric functional data analysis. Theory and practice.
- Properties of principal component methods for functional and longitudinal data analysis
- Mean estimation with data missing at random for functional covariables
- Functional Linear Regression Model for Nonignorable Missing Scalar Responses
- Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
- Components and Completion of Partially Observed Functional Data
- Functional Data Analysis for Sparse Longitudinal Data
- Estimating the Covariance of Fragmented and Other Related Types of Functional Data
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