Estimating the error distribution function in semiparametric additive regression models

DOI10.1016/j.jspi.2011.08.013zbMath1231.62069OpenAlexW1998694645MaRDI QIDQ645626

Anton Schick, Wolfgang Wefelmeyer, Ursula U. Müller

Publication date: 10 November 2011

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.2011.08.013

zbMATH Keywords

Hölder spacelocal polynomial smoothermartingale transform testorthogonal series estimatorpartly linear regression modeltest for normal errorsuniform Bahadur representation

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Order statistics; empirical distribution functions (62G30) Functional limit theorems; invariance principles (60F17)

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Smooth simultaneous confidence band for the error distribution function in nonparametric regression ⋮ Parametric copula adjusted for non- and semiparametric regression ⋮ A semiparametric multivariate partially linear model: a difference approach ⋮ Residual Empirical Processes and Weighted Sums for Time-Varying Processes with Applications to Testing for Homoscedasticity ⋮ Testing for additivity in partially linear regression with possibly missing responses ⋮ Oracally efficient estimation of autoregressive error distribution with simultaneous confidence band ⋮ Inference for conditional value-at-risk of a predictive regression ⋮ Rate of convergence of the density estimation of regression residual ⋮ Strongly consistent density estimation of the regression residual ⋮ Maximum empirical likelihood estimation and related topics ⋮ The transfer principle: a tool for complete case analysis ⋮ Robust functional estimation in the multivariate partial linear model ⋮ The empirical process of residuals from an inverse regression ⋮ Goodness-of-fit testing the error distribution in multivariate indirect regression ⋮ Efficiently estimating the error distribution in nonparametric regression with responses missing at random

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