Robust spectral estimators for long-memory processes- Time and frequency domain approaches

Valdério Anselmo Reisen (DEST-PPGEA-PPGECON-UFES, ES-Brazil)
January 29, 2016 — 10:30 — "Salle des séminaires du L2S (C4.01)"

Abstract

This paper discusses the outlier effects on the estimation of a spectral estimator for long memory process under additive outliers and proposes robust spectral estimators. Some asymptotic properties of the proposed robust methods are derived and Monte Carlo simulations investigate their empirical properties. Pollution series, such as, PM (Particulate matter), SO2 (Sulfur dioxide), are the applied examples investigated here to show the usefulness of the proposed robust methods in real applications. These pollutants present, in general, observations with high levels of pollutant concentrations which may produce sample densities with heavy tails and these high levels of concentrations can be identified as outliers which can destroy the statistical properties of sample functions such as the standard mean, covariance and the periodogram.

Biography

Valderio Anselmo Reisen is full Professor of Statistics at the Federal University of Espirito Santo (UFES), Vitoria, Brazil. His main interests are time series analysis, forecasting, econometric modeling, bootstrap, robustness in time series, unit root processes, counting processes, environmental and economic data analysis, periodically correlated processes, and multivariate time series.