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Sparse Networks Through Regularised Regressions

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Mauro Bernardi

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

  • Michele Costola

    (House of Finance, Goethe University Frankfurt am Main, Research Center SAFE)

Abstract

We propose a Bayesian approach to the problem of variable selection and shrinkage in high dimensional sparse regression models where the regularisation method is an extension of a previous LASSO. The model allows us to include a large number of institutions which improves the identification of the relationship and maintains at the same time the flexibility of the univariate framework. Furthermore, we obtain a weighted directed network since the adjacency matrix is built “row by row” using for each institutions the posterior inclusion probabilities of the other institutions in the system.

Suggested Citation

  • Mauro Bernardi & Michele Costola, 2018. "Sparse Networks Through Regularised Regressions," Springer Books, in: Marco Corazza & María Durbán & Aurea Grané & Cira Perna & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 125-128, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-89824-7_23
    DOI: 10.1007/978-3-319-89824-7_23
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