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.
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Acknowledgements
This research has benefited from the use of the Scientific Computation System of Ca’ Foscari University of Venice (SCSCF) for the computational for the implementation of the inferential procedure.
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Billio, M., Casarin, R., Iacopini, M. (2018). Bayesian Tensor Binary Regression. In: Corazza, M., Durbán, M., Grané, A., Perna, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Cham. https://doi.org/10.1007/978-3-319-89824-7_27
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DOI: https://doi.org/10.1007/978-3-319-89824-7_27
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