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

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Mauro Bernardi

    (University of Padova, Department of Statistical Sciences)

  • Paola Stolfi

    (Istituto per le Applicazioni del Calcolo “Mauro Picone” - CNR
    Roma Tre University, Department of Economics)

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.

Suggested Citation

  • Mauro Bernardi & Paola Stolfi, 2018. "Approximate EM Algorithm for Sparse Estimation of Multivariate Location–Scale Mixture of Normals," Springer Books, in: Marco Corazza & María Durbán & Aurea Grané & Cira Perna & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 129-132, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-89824-7_24
    DOI: 10.1007/978-3-319-89824-7_24
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