Bayesian Tensor Binary Regression

Monica Billio (), Roberto Casarin () and Matteo Iacopini ()
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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

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 143-147 from Springer

Abstract: 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.

Keywords: Tensor binary regression; Sparsity; Bayesian inference; Binary matrices; Hierarchical shrinkage prior (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_27

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DOI: 10.1007/978-3-319-89824-7_27

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