Robust estimation of periodic autoregressive processes in the presence of additive outliers

JOURNAL OF MULTIVARIATE ANALYSIS

卷:101

Article

Sarnaglia, A. J. Q.²  Reisen, V. A.²  Levy-Leduc, C.¹ 

[1] TelecomParisTech, LTCI, CNRS, F-75634 Paris 13, France

[2] Univ Fed Espirito Santo, Dept Estat, Vitoria, ES, Brazil

关键词: Additive outliers;    PAR model;    Periodicity;    Robustness;    Influence function;   

DOI  :  10.1016/j.jmva.2010.05.006

来源: Elsevier

【 摘 要 】

This paper suggests a robust estimation procedure for the parameters of the periodic AR (PAR) models when the data contains additive outliers. The proposed robust methodology is an extension of the robust scale and covariance functions given in, respectively, Rousseeuw and Croux (1993) [28], and Ma and Genton (2000) [23] to accommodate periodicity. These periodic robust functions are used in the Yule-Walker equations to obtain robust parameter estimates. The asymptotic central limit theorems of the estimators are established, and an extensive Monte Carlo experiment is conducted to evaluate the performance of the robust methodology for periodic time series with finite sample sizes. The quarterly Fraser River data was used as an example of application of the proposed robust methodology. All the results presented here give strong motivation to use the methodology in practical situations in which periodically correlated time series contain additive outliers. (c) 2010 Elsevier Inc. All rights reserved.

【 授权许可】

Free