Approximate EM Algorithm for Sparse Estimation of Multivariate Location–Scale Mixture of Normals

Mauro Bernardi () and Paola Stolfi ()
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Mauro Bernardi: University of Padova, Department of Statistical Sciences
Paola Stolfi: Istituto per le Applicazioni del Calcolo “Mauro Picone” - CNR

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 129-132 from Springer

Abstract: Abstract Parameter estimation of distributions with intractable density, such as the Elliptical Stable, often involves high-dimensional integrals requiring numerical integration or approximation. This paper introduces a novel Expectation–Maximisation algorithm for fitting such models that exploits the fast Fourier integration for computing the expectation step. As a further contribution we show that by slightly modifying the objective function, the proposed algorithm also handle sparse estimation of non-Gaussian models. The method is subsequently applied to the problem of selecting the asset within a sparse non-Gaussian portfolio optimisation framework.

Keywords: Sparse estimation; Multivariate heavy-tailed distributions; Expectation maximisation; Portfolio optimisation (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-89824-7_24

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DOI: 10.1007/978-3-319-89824-7_24

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