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.
Billio, Monica; Casarin, Roberto; Iacopini, Matteo. (2018). Bayesian Tensor Binary Regression. In Marco Corazza, María Durbán, Aurea Grané, Cira Perna, Marilena Sibillo (Eds.), Mathematical and Statistical Methods for Actuarial Sciences and Finance (pp. 143-147). Springer. Isbn: 978-3-319-89823-0. Doi: 10.1007/978-3-319-89824-7_27.
Bayesian Tensor Binary Regression
Matteo Iacopini
Formal Analysis
2018
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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