Empirical process of residuals for regression models with long memory errors

DOI10.1016/j.spl.2013.11.018zbMath1288.62128arXiv1102.4368OpenAlexW2041796555MaRDI QIDQ2452780

Rafał Kulik, Paweł Lorek

Publication date: 5 June 2014

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1102.4368

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Design of statistical experiments (62K99)

Related Items (1)

Smooth Estimation of Error Distribution in Nonparametric Regression Under Long Memory

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Residual empirical processes for long and short memory time series
• Goodness-of-fit testing under long memory
• Estimating the innovation distribution in nonparametric autoregression
• Asymptotic normality of regression estimators with long memory errors
• A weak convergence result useful in robust autoregression
• Asymptotics of R-, MD- and LAD-estimators in linear regression models with long range dependent errors
• Weak convergence of randomly weighted dependent residual empiricals with applications to autoregression
• Asymptotic expansion of \(M\)-estimators with long-memory errors
• Empirical processes of long-memory sequences
• Weighted empirical processes in dynamic nonlinear models.
• On the asymptotic expansion of the empirical process of long-memory moving averages
• Weak convergence of the sequential empirical processes of residuals in ARMA models
• Second-order behavior of M-estimators in linear regression with long-memory errors

This page was built for publication: Empirical process of residuals for regression models with long memory errors