Rank estimation for the functional linear model

Melody Denhere and Huybrechts F. Bindele

Journal of Applied Statistics, 2016, vol. 43, issue 10, 1928-1944

Abstract: This article discusses the estimation of the parameter function for a functional linear regression model under heavy-tailed errors' distributions and in the presence of outliers. Standard approaches of reducing the high dimensionality, which is inherent in functional data, are considered. After reducing the functional model to a standard multiple linear regression model, a weighted rank-based procedure is carried out to estimate the regression parameters. A Monte Carlo simulation and a real-world example are used to show the performance of the proposed estimator and a comparison made with the least-squares and least absolute deviation estimators.

Date: 2016
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DOI: 10.1080/02664763.2015.1125863

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