Empirical study of robust estimation methods for PAR models with application to the air quality area

Carlo Correa Solci (1) , Valdério A. Reisen (1) , Alessandro Jose Queiroz Sarnaglia (1) , Pascal Bondon (2)

1 UFES - Universidade Federal do Espirito Santo
2 L2S - Laboratoire des signaux et systèmes

This paper compares three estimators for periodic autoregressive (PAR) models. The first is the classical periodic Yule-Walker estimator (YWE). The second is a robust version of YWE (RYWE) which uses the robust autocovariance function in the periodic Yule-Walker equations, and the third is the robust least squares estimator (RLSE) based on iterative least squares with robust versions of the original time series. The daily mean particulate matter concentration (PM10) data is used to illustrate the methodologies in a real application, that is, in the Air Quality area.

Keywords: Robust estimation; PAR models; Outliers; PM10 pollutant

Domains: Statistics [stat]; Statistics Theory [stat.TH]; Statistics [stat]; Applications [stat.AP]; Physics [physics]; Physics [physics]; Atmospheric and Oceanic Physics [physics.ao-ph]; Environmental Sciences; Environmental Engineering

Carlo Correa Solci, Valdério A. Reisen, Alessandro Jose Queiroz Sarnaglia, Pascal Bondon. Empirical study of robust estimation methods for PAR models with application to the air quality area. Communications in Statistics - Theory and Methods, 2020, 49 (1), pp.152-168. ⟨10.1080/03610926.2018.1533970⟩. ⟨hal-01886198⟩