Bayesian Markov-Switching Tensor Regression for Time-Varying Networks
From MaRDI portal
Publication:6153973
DOI10.1080/01621459.2022.2102502arXiv1711.00097OpenAlexW3125143186WikidataQ114101022 ScholiaQ114101022MaRDI QIDQ6153973
Roberto Casarin, Monica Billio, Matteo Iacopini
Publication date: 19 March 2024
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1711.00097
Cites Work
- Unnamed Item
- Unnamed Item
- Tensor Decompositions and Applications
- A zero-inflated ordered probit model, with an application to modelling tobacco consumption
- Bayesian dynamic financial networks with time-varying predictors
- Multilinear tensor regression for longitudinal relational data
- A state-space mixed membership blockmodel for dynamic network tomography
- A review of dynamic network models with latent variables
- Modeling systemic risk with Markov switching graphical SUR models
- Discrete temporal models of social networks
- The geometry of continuous latent space models for network data
- Adaptive higher-order spectral estimators
- On the network topology of variance decompositions: measuring the connectedness of financial firms
- Finite mixture and Markov switching models.
- Bayesian nonhomogeneous Markov models via Pólya-Gamma data augmentation with applications to rainfall modeling
- Separable covariance arrays via the Tucker product, with applications to multivariate relational data
- Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
- Tensor Spaces and Numerical Tensor Calculus
- Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models
- Tensor Generalized Estimating Equations for Longitudinal Imaging Analysis
- The Bayesian Choice
- Tensor Regression with Applications in Neuroimaging Data Analysis
- Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
- The Distribution of Products of Beta, Gamma and Gaussian Random Variables
- A Separable Model for Dynamic Networks
- Variance Estimation in a Model With Gaussian Submodels
- Deviance information criteria for missing data models