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Parameter Estimation of Autoregressive Models Using the Iteratively Robust Filtered Fast-τ Method

Shariati Fokalaei, Nima LU ; Shahriari, Hamid and Shafaei, Rasoul (2014) In Communications in Statistics: Theory and Methods 43(21). p.4445-4470
Abstract
Utilizing time series modeling entails estimating the model parameters and dispersion. Classical estimators for autocorrelated observations are sensitive to presence of different types of outliers and lead to bias estimation and misinterpretation. It is important to present robust methods for parameters estimation which are not influenced by contaminations. In this article, an estimation method entitled Iteratively Robust Filtered Fast− τ(IRFFT) is proposed for general autoregressive models. In comparison to other commonly accepted methods, this method is more efficient and has lower sensitivity to contaminations due to having desirable robustness properties. This has been demonstrated by applying MSE, influence function, and breakdown... (More)
Utilizing time series modeling entails estimating the model parameters and dispersion. Classical estimators for autocorrelated observations are sensitive to presence of different types of outliers and lead to bias estimation and misinterpretation. It is important to present robust methods for parameters estimation which are not influenced by contaminations. In this article, an estimation method entitled Iteratively Robust Filtered Fast− τ(IRFFT) is proposed for general autoregressive models. In comparison to other commonly accepted methods, this method is more efficient and has lower sensitivity to contaminations due to having desirable robustness properties. This has been demonstrated by applying MSE, influence function, and breakdown point criteria. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Time series, Autocorrelation, Robust estimation, Outlier, Robust filtering, τ− estimate
in
Communications in Statistics: Theory and Methods
volume
43
issue
21
pages
4445 - 4470
publisher
Marcel Dekker
external identifiers
  • scopus:84910619350
ISSN
0361-0926
DOI
10.1080/03610926.2012.724504
language
English
LU publication?
yes
id
600d52e0-ef9c-4c76-9453-2278dcb24608 (old id 5368076)
date added to LUP
2015-05-11 15:18:53
date last changed
2017-03-26 03:16:16
@article{600d52e0-ef9c-4c76-9453-2278dcb24608,
  abstract     = {Utilizing time series modeling entails estimating the model parameters and dispersion. Classical estimators for autocorrelated observations are sensitive to presence of different types of outliers and lead to bias estimation and misinterpretation. It is important to present robust methods for parameters estimation which are not influenced by contaminations. In this article, an estimation method entitled Iteratively Robust Filtered Fast− τ(IRFFT) is proposed for general autoregressive models. In comparison to other commonly accepted methods, this method is more efficient and has lower sensitivity to contaminations due to having desirable robustness properties. This has been demonstrated by applying MSE, influence function, and breakdown point criteria.},
  author       = {Shariati Fokalaei, Nima and Shahriari, Hamid and Shafaei, Rasoul},
  issn         = {0361-0926},
  keyword      = {Time series,Autocorrelation,Robust estimation,Outlier,Robust filtering,τ− estimate},
  language     = {eng},
  number       = {21},
  pages        = {4445--4470},
  publisher    = {Marcel Dekker},
  series       = {Communications in Statistics: Theory and Methods},
  title        = {Parameter Estimation of Autoregressive Models Using the Iteratively Robust Filtered Fast-τ Method},
  url          = {http://dx.doi.org/10.1080/03610926.2012.724504},
  volume       = {43},
  year         = {2014},
}