In this paper we introduce the literature on regression models with tensor variables and present a Bayesian linear model for inference, under the assumption 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 via Gibbs sampler for carrying out the estimation, discussing the issues related to the initialisation of the vectors of parameters involved in the CP representation.
Autori: | Matteo Iacopini (Corresponding) |
Data di pubblicazione: | 2018 |
Titolo: | Bayesian Tensor Regression Models |
Titolo del libro: | Mathematical and Statistical Methods for Actuarial Sciences and Finance |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1007/978-3-319-89824-7 |
Appare nelle tipologie: | 3.1 Articolo su libro |
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Proceedings MAF2018 - Chapter 29.pdf | N/A | Accesso chiuso-personale | Riservato |
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