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Bayesian Tensor Binary Regression

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Monica Billio

    (Ca’ Foscari University of Venice, Department of Economics)

  • Roberto Casarin

    (Ca’ Foscari University of Venice, Department of Economics)

  • Matteo Iacopini

    (Ca’ Foscari University of Venice, Department of Economics
    Université Paris 1 - Panthéon-Sorbonne)

Abstract

In this paper we present a binary regression model with tensor coefficients and present a Bayesian model for inference, able to recover different levels of sparsity of the tensor coefficient. We exploit the CONDECOMP/PARAFAC (CP) representation for the tensor of coefficients in order to reduce the number of parameters and adopt a suitable hierarchical shrinkage prior for inducing sparsity. We propose a MCMC procedure with data augmentation for carrying out the estimation and test the performance of the sampler in small simulated examples.

Suggested Citation

  • Monica Billio & Roberto Casarin & Matteo Iacopini, 2018. "Bayesian Tensor Binary Regression," Springer Books, in: Marco Corazza & María Durbán & Aurea Grané & Cira Perna & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 143-147, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-89824-7_27
    DOI: 10.1007/978-3-319-89824-7_27
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