Prediction and nonparametric estimation for time series with heavy tails

DOI10.1111/1467-9892.00266zbMath1022.62078OpenAlexW3122317270MaRDI QIDQ4431622

Qiwei Yao, Hall, Peter, Liang Peng

Publication date: 22 October 2003

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: http://eprints.lse.ac.uk/6086/

zbMATH Keywords

strong mixingrho-mixingARMA modelstable distributionconditional meanleast absolute deviation estimationlocal-linear regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20)

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Uses Software

• KernSmooth

Cites Work

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• Nonparametric regression with long-range dependence
• Pitman estimation on \({\mathbb{R}}^ k\)
• Some mixing properties of time series models
• Linear prediction of ARMA processes with infinite variance
• Heavy tail modeling and teletraffic data. (With discussions and rejoinder)
• Large-sample inference for nonparametric regression with dependent errors
• Nonparametric regression with long-memory errors
• Fixed-design regression for linear time series