Smooth backfitting for additive modeling with small errors-in-variables, with an application to additive functional regression for multiple predictor functions

DOI10.3150/16-BEJ898zbMath1419.62088MaRDI QIDQ2405219

Kyunghee Han, Byeong U. Park, Hans-Georg Müller

Publication date: 21 September 2017

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.bj/1505980895

zbMATH Keywords

kernel smoothingfunctional data analysissmooth backfittingfunctional principal componenterrors in predictorsfunctional additive model

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Factor analysis and principal components; correspondence analysis (62H25) General nonlinear regression (62J02)

Related Items (6)

Hilbertian additive regression with parametric help ⋮ Additive regression with Hilbertian responses ⋮ Additive Functional Regression for Densities as Responses ⋮ Additive regression for non-Euclidean responses and predictors ⋮ Smooth backfitting for errors-in-variables varying coefficient regression models ⋮ Nonparametric and high-dimensional functional graphical models

This page was built for publication: Smooth backfitting for additive modeling with small errors-in-variables, with an application to additive functional regression for multiple predictor functions