Some results on random design regression with long memory errors and predictors
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Publication:710817
DOI10.1016/j.jspi.2010.06.030zbMath1197.62037arXiv1102.4372OpenAlexW2963324828MaRDI QIDQ710817
Publication date: 22 October 2010
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1102.4372
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Related Items (7)
Conditional variance estimation in regression models with long memory ⋮ Insensitivity of Nadaraya–Watson estimators to design correlation ⋮ On sufficient conditions for the consistency of local linear kernel estimators ⋮ Towards Insensitivity of Nadaraya--Watson Estimators to Design Correlation ⋮ Universal kernel-type estimation of random fields ⋮ Nonparametric conditional variance and error density estimation in regression models with dependent errors and predictors ⋮ Universal weighted kernel-type estimators for some class of regression models
Cites Work
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- Limit theorems for functionals of moving averages
- Asymptotic results for long memory LARCH sequences
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- Local polynomial fitting with long-memory, short-memory and antipersistent errors
- On bandwidth choice in nonparametric regression with both short- and long-range dependent errors
- On bandwidth choice for density estimation with dependent data
- Nonparametric deconvolution problem for dependent sequences
- How to overcome the curse of long-memory errors
- Kernel density estimation for linear processes
- Local polynomial estimation with a FARIMA-GARCH error process
- Nonparametric regression with dependent errors
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