Title	Robust Depth-based Estimation of the Functional Autoregressive Model
Creator	Martinez Hernandez, Israel
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2017-09-04T17:05
Description	We propose a robust estimator for functional autoregressive models. This estimator, the Depth-based Least Squares (DLS) estimator, down-weights the influence of outliers by using the functional outlyingness as a centrality measure. The DLS estimator consists of two steps: identifying the outliers with a functional boxplot based on a defined depth, then down-weighting the outliers using the functional outlyingness. We prove that the influence function of the DLS estimator is bounded. Through a Monte Carlo study, we show that the DLS estimator performs better than the PCA and robust PCA estimators, which are the most commonly used.
Extent	25 minutes
Subject	Mathematics; Statistics; Partial differential equations
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Centro de Investigación en Matemáticas, CIMAT
Series	BIRS Workshop Lecture Videos (Oaxaca de Juárez (Mexico))
Date Available	2018-03-28
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0364512
URI	http://hdl.handle.net/2429/64970
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Graduate
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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