Robust signal dimension estimation via SURE

The estimation of signal dimension under heavy-tailed latent variable 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...

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Bibliographic Details
Published inStatistical papers (Berlin, Germany) Vol. 65; no. 5; pp. 3007 - 3038
Main Authors Virta, Joni, Lietzén, Niko, Nyberg, Henri
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2024
Springer Nature B.V
Subjects
Datasets
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Finance
Insurance
Management
Mathematics and Statistics
Normal distribution
Operations Research/Decision Theory
Principal components analysis
Probability Theory and Stochastic Processes
Regular Article
Robustness
Statistics
Statistics for Business
Risk estimate
Scatter matrix
Elliptical distribution
Principal component analysis
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Summary:The estimation of signal dimension under heavy-tailed latent variable 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.
ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-023-01512-2