Open Access
2022 Robust sieve M-estimation with an application to dimensionality reduction
Julien Bodelet, Davide La Vecchia
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Electron. J. Statist. 16(2): 3996-4030 (2022). DOI: 10.1214/22-EJS2038


We propose a sieve M-estimation procedure which combines the flexibility of semiparametric inference with the stability and reliability of infinitesimal robustness. We derive the asymptotic theory of the proposed estimators, studying their convergence rate. In the context of functional magnetic resonance imaging (fMRI) data analysis, we illustrate how to apply our procedure to conduct inference on a semiparametric dynamic factor model. Monte Carlo simulations and real data analysis exemplify the stability of our estimators, providing a comparison with the extant, non robust and routinely applied sieve M-estimators.


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Julien Bodelet. Davide La Vecchia. "Robust sieve M-estimation with an application to dimensionality reduction." Electron. J. Statist. 16 (2) 3996 - 4030, 2022.


Received: 1 May 2021; Published: 2022
First available in Project Euclid: 26 July 2022

MathSciNet: MR4456782
zbMATH: 1493.62155
Digital Object Identifier: 10.1214/22-EJS2038

Primary: 62F35 , 62G05
Secondary: 62H25

Keywords: dynamic factor model , functional magnetic resonance imaging , Huber loss function , Outliers , semiparametric modeling

Vol.16 • No. 2 • 2022
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