Additive regression for predictors of various natures and possibly incomplete Hilbertian responses

DOI10.1214/21-EJS1823zbMath1471.62329OpenAlexW3139266520MaRDI QIDQ2044344

Byeong U. Park, Jeong Min Jeon, Ingrid Van Keilegom

Publication date: 9 August 2021

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/21-ejs1823

zbMATH Keywords

additive modelmixed predictorsmooth backfittingincomplete responseHilbertian response

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20)

Related Items (1)

Additive regression for non-Euclidean responses and predictors

Uses Software

• fda (R)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Simplicial principal component analysis for density functions in Bayes spaces
• The existence and asymptotic properties of a backfitting projection algorithm under weak conditions
• Flexible generalized varying coefficient regression models
• Projection-type estimation for varying coefficient regression models
• Regression when both response and predictor are functions
• Semi-parametric regression: efficiency gains from modeling the nonparametric part
• A note on the backfitting estimation of additive models
• Backfitting and smooth backfitting for additive quantile models
• New perspectives on water chemistry and compositional data analysis
• A simple smooth backfitting method for additive models
• A note on the uniform consistency of the Kaplan-Meier estimator
• Regression analysis with randomly right-censored data
• Fitting a bivariate additive model by local polynomial regression
• Asymptotic properties of backfitting estimators
• Linear processes in function spaces. Theory and applications
• Applied functional data analysis. Methods and case studies
• Compositional regression with functional response
• Smooth backfitting for errors-in-variables additive models
• Convergence of functional \(k\)-nearest neighbor regression estimate with functional responses
• Central limit theorems revisited
• Additive regression with Hilbertian responses
• The product-limit estimator and the bootstrap: Some asymptotic representations
• Compositional data: the sample space and its structure
• Estimation of a semiparametric transformation model
• Varying coefficient models having different smoothing variables with randomly censored data
• Smooth backfitting in generalized additive models
• On infinite-dimensional convex programs
• Bayes Hilbert Spaces
• Theoretical Numerical Analysis
• Multivariate binary discrimination by the kernel method
• MULTIVARIATE LOCAL POLYNOMIAL REGRESSION FOR TIME SERIES:UNIFORM STRONG CONSISTENCY AND RATES
• Smooth Backfitting in Practice
• A kernel method of estimating structured nonparametric regression based on marginal integration
• Non-parametric regression for compositional data
• Wasserstein covariance for multiple random densities
• Additive Functional Regression for Densities as Responses
• Measure Theory
• Functional data analysis for density functions by transformation to a Hilbert space

This page was built for publication: Additive regression for predictors of various natures and possibly incomplete Hilbertian responses