Estimating the innovation distribution in nonparametric autoregression

DOI10.1007/S00440-008-0141-2zbMath1160.62035OpenAlexW1981563775MaRDI QIDQ1017896

Ursula U. Müller, Wolfgang Wefelmeyer, Anton Schick

Publication date: 13 May 2009

Published in: Probability Theory and Related Fields (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00440-008-0141-2

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Order statistics; empirical distribution functions (62G30) Markov processes: estimation; hidden Markov models (62M05) Functional limit theorems; invariance principles (60F17)

Related Items (12)

Distribution-free testing in linear and parametric regression ⋮ Smooth Estimation of Error Distribution in Nonparametric Regression Under Long Memory ⋮ Asymptotics of estimators for nonparametric multivariate regression models with long memory ⋮ Residual Empirical Processes and Weighted Sums for Time-Varying Processes with Applications to Testing for Homoscedasticity ⋮ Estimating the Error Distribution in a Single-Index Model ⋮ Estimating the error distribution function in semiparametric additive regression models ⋮ Empirical process of residuals for regression models with long memory errors ⋮ Specification testing in nonparametric AR‐ARCH models ⋮ Lack of fit test for long memory regression models ⋮ A note on non-parametric testing for Gaussian innovations in AR-ARCH models ⋮ Testing for a Change of the Innovation Distribution in Nonparametric Autoregression: The Sequential Empirical Process Approach ⋮ Goodness-of-fit testing of error distribution in nonparametric ARCH(1) models

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