Nonparametric M-estimation with long-memory errors

DOI10.1016/S0378-3758(02)00391-9zbMath1021.62025OpenAlexW2245792427MaRDI QIDQ1410279

Sucharita Ghosh, Philipp Sibbertsen, Jan Beran

Publication date: 14 October 2003

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(02)00391-9

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Central limit and other weak theorems (60F05)

Related Items

Robust estimation in a nonlinear cointegration model ⋮ Asymptotic properties of wavelet estimators in partially linear errors-in-variables models with long-memory errors ⋮ Asymptotic properties for an M-estimator of the regression function with truncation and dependent data ⋮ Asymptotic properties of nonparametric M-estimation for mixing functional data ⋮ Long memory versus structural breaks: an overview ⋮ Asymptotic expansion for nonparametric M-estimator in a nonlinear regression model with long-memory errors ⋮ M-estimation in nonparametric regression under strong dependence and infinite variance

Uses Software

• longmemo

Cites Work

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• Convergence rates in density estimation for data from infinite-order moving average processes
• Asymptotic maximal deviation of M-smoothers
• Robust nonparametric regression estimation for dependent observations
• Nonparametric regression under long-range dependent normal errors
• Fractional differencing
• AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
• M Estimators of Location for Gaussian and Related Processes With Slowly Decaying Serial Correlations
• Weak convergence to fractional brownian motion and to the rosenblatt process
• Convergence of integrated processes of arbitrary Hermite rank
• Fractional Brownian Motions, Fractional Noises and Applications
• Nonparametric trend estimation in replicated time series