Outlier detection and trimmed estimation for general functional data

TL;DR

This paper develops robust trimmed estimators for the mean and covariance of functional data using a new outlier measure, demonstrating improved outlier resistance and practical utility in real-data applications.

Contribution

It introduces a novel outlyingness measure applicable to any metric space and develops trimmed estimators with optimal breakdown points for functional data.

Findings

Estimators have higher breakdown points than existing methods.

Simulation shows improved outlier resistance.

Real-data applications confirm practical effectiveness.

Abstract

This article introduces trimmed estimators for the mean and covariance function of general functional data. The estimators are based on a new measure of outlyingness or data depth that is well defined on any metric space, although this paper focuses on Euclidean spaces. We compute the breakdown point of the estimators and show that the optimal breakdown point is attainable for the appropriate choice of tuning parameters. The small-sample behavior of the estimators is studied by simulation, and we show that they have better outlier-resistance properties than alternative estimators. This is confirmed by two real-data applications, that also show that the outlyingness measure can be used as a graphical outlier-detection tool in functional spaces where visual screening of the data is difficult.