Bayesian Variable Selection in a Large Vector Autoregression for Origin-Destination Traffic Flow Modelling

DOI10.1007/978-3-319-30196-9_10zbMath1402.62335OpenAlexW2506977558MaRDI QIDQ4555376

Minfeng Deng

Publication date: 20 November 2018

Published in: Advances in Spatial Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-30196-9_10

zbMATH Keywords

trafficvariable selectionBayesianvector autoregressive model

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15)

Cites Work

• Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
• A Three-Stage Iterative Procedure for Space-Time Modeling
• Using spatial contiguity as prior information in vector autoregressive models
• Nonparametric regression using Bayesian variable selection
• Bayesian inference for a covariance matrix
• Aggregation of space-time processes.
• Trade flows and spatial effects: The gravity model revisited
• Estimation of a covariance matrix using the reference prior
• Empirical Bayes vs. fully Bayes variable selection
• Mixtures of g Priors for Bayesian Variable Selection
• Identification and Interpretation of First Order Space-Time ARMA Models
• Variance of the Sample Space-Time Correlation Function of Contemporaneously Correlated Variables
• Multivariate Bayesian Variable Selection and Prediction
• Forecasting and conditional projection using realistic prior distributions
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