Smooth backfitting for errors-in-variables additive models

DOI10.1214/17-AOS1617zbMath1404.62038WikidataQ129379561 ScholiaQ129379561MaRDI QIDQ1800800

Byeong U. Park, Kyunghee Han

Publication date: 24 October 2018

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aos/1534492834

zbMATH Keywords

kernel smoothingdeconvolutionerrors-in-variables modelssmooth backfittingmultivariate deconvolutionnonparametric additive regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20)

Related Items (7)

Locally polynomial Hilbertian additive regression ⋮ Local linear smoothing in additive models as data projection ⋮ Nonparametric estimation of additive models with errors-in-variables ⋮ Additive regression with Hilbertian responses ⋮ Additive regression for predictors of various natures and possibly incomplete Hilbertian responses ⋮ Additive regression for non-Euclidean responses and predictors ⋮ Smooth backfitting for errors-in-variables varying coefficient regression models

Cites Work

• Unnamed Item
• 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
• Adaptive density estimation in deconvolution problems with unknown error distribution
• Backfitting and smooth backfitting for additive quantile models
• Strong consistency and rates for deconvolution of multivariate densities of stationary processes
• On the optimal rates of convergence for nonparametric deconvolution problems
• Estimation of distributions, moments and quantiles in deconvolution problems
• Additive regression and other nonparametric models
• Multivariate regression estimation with errors-in-variables: Asymptotic normality for mixing processes
• Nonparametric regression with errors in variables
• Estimation in a semiparametric partially linear errors-in-variables model
• On deconvolution with repeated measurements
• Estimation of a semiparametric transformation model
• Smooth backfitting in generalized additive models
• Generalised structured models
• Data-Driven Density Estimation in the Presence of Additive Noise with unknown Distribution
• Corrected local polynomial estimation in varying-coefficient models with measurement errors
• Deconvolving kernel density estimators
• A Semi‐parametric Regression Model with Errors in Variables
• Inference for a leptokurtic symmetric family of distributions represented by the difference of two gamma variates
• A note on gamma difference distributions
• Nonparametric Prediction in Measurement Error Models
• A Design-Adaptive Local Polynomial Estimator for the Errors-in-Variables Problem
• Adaptive quantile estimation in deconvolution with unknown error distribution

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