A robust estimation approach for fitting a PARMA model to real data

Alessandro Jose Queiroz Sarnaglia 1 Valderio Anselmo Reisen 1, 2 Pascal Bondon 3 Céline Lévy-Leduc 4
1 Graduate Program in Statistics
2 Universade Federal Do Espirito Santo
3 L2S - Laboratoire des signaux et systèmes
4 MIA-Paris - Mathématiques et Informatique Appliquées
Abstract : This paper proposes an estimation approach of the Whittle estimator to fit periodic autoregressive moving average (PARMA) models when the process is contaminated with additive outliers and/or has heavy-tailed noise. It is derived by replacing the ordinary Fourier transform with the non-linear M-regression estimator in the harmonic regression equation that leads to the classical periodogram. A Monte Carlo experiment is conducted to study the finite sample size of the proposed estimator under the scenarios of contaminated and non-contaminated series. The proposed estimation method is applied to fit a PARMA model to the sulfur dioxide (SO2) daily average pollutant concentrations in the city of Vitória (ES), Brazil.
Type de document :
Communication dans un congrès
2016 IEEE Statistical Signal Processing Workshop (SSP), Jun 2016, Palma de Mallorca, Spain. 5 p., 〈10.1109/ssp.2016.7551740 〉
Liste complète des métadonnées
https://hal-agroparistech.archives-ouvertes.fr/hal-01560258
Contributeur : Armelle Sielinou <>
Soumis le : mardi 11 juillet 2017 - 14:16:30
Dernière modification le : mardi 2 octobre 2018 - 15:14:02

Identifiants

Collections

AGROPARISTECH | INRA | L2S | MIA-PARIS | CENTRALESUPELEC | SUP_LSS | UNIV-PARIS-SACLAY | SUP_SIGNAUX | INSMI

Citation

Alessandro Jose Queiroz Sarnaglia, Valderio Anselmo Reisen, Pascal Bondon, Céline Lévy-Leduc. A robust estimation approach for fitting a PARMA model to real data. 2016 IEEE Statistical Signal Processing Workshop (SSP), Jun 2016, Palma de Mallorca, Spain. 5 p., 〈10.1109/ssp.2016.7551740 〉. 〈hal-01560258〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

298

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD