Bayesian Tensor Regression Models

Monica Billio (), Roberto Casarin () and Matteo Iacopini ()
Additional contact information
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 149-153 from Springer

Abstract: Abstract 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.

Keywords: Tensor regression; Sparsity; Bayesian inference; Hierarchical shrinkage prior (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-89824-7_28

Ordering information: This item can be ordered from
http://www.springer.com/9783319898247

DOI: 10.1007/978-3-319-89824-7_28

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().