2017/10/31 by Monica Billio, Billio, Monica, Roberto Casarin +3 · 1 citation
Mathematics · Medicine · Physics and Astronomy · #Advanced Neuroimaging Techniques and Applications #Complex Network Analysis Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Tensor decomposition and applications
paper · pdf · doi:10.48550/arxiv.1711.00097
openalex publication_date 2017/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a new Bayesian Markov switching regression model for multidimensional arrays (tensors) of binary time series. We assume a zero-inflated logit regression with time-varying parameters and apply it to multilayer temporal networks. The original contribution is threefold. First, to avoid over-fitting we propose a parsimonious parametrization based on a low-rank decomposition of the tensor of regression coefficients. Second, we assume the parameters are driven by a hidden Markov chain, thus allowing for structural changes in the network topology. We follow a Bayesian approach to inference and provide an efficient Gibbs sampler for posterior approximation. We apply the methodology to a real dataset of financial networks to study the impact of several risk factors on the edge probability. Supplementary materials for this article are available online.