Robust signal dimension estimation via SURE

TL;DR

This paper introduces robust extensions of a signal dimension estimator for heavy-tailed data using elliptical distributions and robust scatter matrices, demonstrating improved accuracy and speed through simulations and financial data application.

Contribution

It proposes novel robust extensions of an existing estimator based on elliptical distributions and robust scatter matrices for heavy-tailed data.

Findings

Robust estimators outperform traditional methods in accuracy.

New methods are computationally efficient.

Effective application to financial asset return data.

Abstract

The estimation of signal dimension under heavy-tailed latent factor models is studied. As a primary contribution, robust extensions of an earlier estimator based on Gaussian Stein's unbiased risk estimation are proposed. These novel extensions are based on the framework of elliptical distributions and robust scatter matrices. Extensive simulation studies are conducted in order to compare the novel methods with several well-known competitors in both estimation accuracy and computational speed. The novel methods are applied to a financial asset return data set.