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Robust Two-Step Wavelet-Based Inference for Time Series Models by Guerrier, Stéphane; Molinari, Roberto; Victoria-Feser, Maria-Pia; Xu, Haotian is a scholarly article available to read on EtoBox.

What is Robust Two-Step Wavelet-Based Inference for Time Series Models about?

Complex time series models such as (the sum of) ARMA$(p,q)$ models with additional noise, random walks, rounding errors and/or drifts are increasingly used for data analysis in fields such as biology, ecology, engineering and economics where the length of the observed signals can be extremely large. Performing inference on and/or prediction from these models can be highly challenging for several reasons: (i) the data may contain outliers that can adversely affect the estimation procedure; (ii) the computational complexity can become prohibitive when models include more than just a few parameters and/or the time series are large; (iii) model building and/or selection adds another layer of (computational) complexity to the previous task; and (iv) solutions that address (i), (ii) and (iii) simultaneously do not exist in practice. For this reason, this paper aims at jointly addressing these challenges by proposing a general framework for robust two-step estimation based on a bounded influence M-estimator of the wavelet variance. In this perspective, we first develop the conditions for the joint asymptotic normality of the latter estimator thereby providing the necessary tools to perfor

Author

Guerrier, Stéphane; Molinari, Roberto; Victoria-Feser, Maria-Pia; Xu, Haotian

Published

2020

Language

EN