Robust Depth-based Estimation of the Functional Autoregressive Model

"Robust Depth-based Estimation of the Functional Autoregressive Model", organizado polo CITMAga. Será impartido por Israel Martínez Hernández,  Department of Mathematics and Statistics (Lancaster University)

Data: mércores 3 de febreiro

Hora: 13:00 h.

Duración: 1 hora

Lugar: Online (MS Teams)

Abstract:

I will talk about a robust estimator for functional autoregressive models, the Depth-based Least Squares (DLS) estimator. The DLS estimator down-weights the influence of outliers by using the functional directional outlyingness as a centrality measure. It consists of two steps: identifying the outliers with a two-stage functional boxplot, then down-weighting the outliers using the functional directional outlyingness. To illustrate a practical application, the DLS estimator is used to analyze a dataset of ambient CO2 concentrations in California.