Strong selection consistency of Bayesian vector autoregressive models based on a pseudo-likelihood approach

DOI10.1214/20-AOS1992zbMath1479.62071OpenAlexW3188631473MaRDI QIDQ820793

George Michailidis, Kshitij Khare, Satyajit Ghosh

Publication date: 28 September 2021

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/20-aos1992

zbMATH Keywords

high-dimensional datapseudo-likelihoodvector autoregressionBayesian variable selectionstrong selection consistency

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Ridge regression; shrinkage estimators (Lasso) (62J07) Bayesian inference (62F15)

Related Items

Discussion to: \textit{Bayesian graphical models for modern biological applications} by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo ⋮ The EAS approach for graphical selection consistency in vector autoregression models ⋮ A Bayesian Framework for Sparse Estimation in High-Dimensional Mixed Frequency Vector Autoregressive Models ⋮ The Bayesian nested Lasso for mixed frequency regression models ⋮ Discussion of “A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks” by Pavel N. Krivitsky, Pietro Coletti, and Niel Hens

Uses Software

• SSS

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Regularized estimation in sparse high-dimensional time series models
• Bayesian variable selection with shrinking and diffusing priors
• Forecasting using a large number of predictors: is Bayesian shrinkage a valid alternative to principal components?
• Hanson-Wright inequality and sub-Gaussian concentration
• Bayesian linear regression with sparse priors
• Consistency of Bayesian procedures for variable selection
• Posterior graph selection and estimation consistency for high-dimensional Bayesian DAG models
• High-dimensional posterior consistency for hierarchical non-local priors in regression
• Spike and slab variable selection: frequentist and Bayesian strategies
• Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues
• Calibration and empirical Bayes variable selection
• Statistical learning and selective inference
• Mixtures of g Priors for Bayesian Variable Selection
• Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings
• High-Dimensional Probability
• Bayes Model Averaging with Selection of Regressors
• Consistent High-Dimensional Bayesian Variable Selection via Penalized Credible Regions
• Bayesian Model Selection in High-Dimensional Settings
• High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models
• Shotgun Stochastic Search for “Largep” Regression
• A Split-and-Merge Bayesian Variable Selection Approach for Ultrahigh Dimensional Regression
• Fixed and Random Effects Selection in Linear and Logistic Models