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Sparse Networks Through Regularised Regressions

  • Mauro Bernardi
  • Michele Costola
Chapter

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.

Keywords

Financial networks Sparsity Bayesian inference 

Notes

Acknowledgements

The author acknowledges financial support from the Marie Skłodowska-Curie Actions, European Union, Seventh Framework Program HORIZON 2020 under REA grant agreement n.707070. He also gratefully acknowledges research support from the Research Center SAFE, funded by the State of Hessen initiative for research LOEWE.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Statistical SciencesUniversity of PadovaRomaItaly
  2. 2.Istituto per le Applicazioni del Calcolo “Mauro Picone” - CNRRomaItaly
  3. 3.Research Center SAFEHouse of Finance, Goethe University Frankfurt am MainFrankfurtGermany

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