The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singular multivariate beta distributions allowing a fully conjugate Bayesian infere...

While considerable advances have been made in estimating high-dimensional structured models from independent data using Lasso-type models, limited progress has been made for settings when the samples are dependent. We consider estimating structured VAR (vector auto-regressive model), where the structure can be captured by any suitable norm, e.g., Lasso, group Lasso, order weighted Lasso, etc. I...

We present a complete Bayesian treatment of autoregressive model estimation incorporating choice of autoregressive order, enforcement of stationarity, treatment of outliers and allowance for missing values and multiplicative seasonality. The paper makes three distinct contributions. First, we enforce the stationarity conditions using a very eecient Metropolis-within-Gibbs algorithm to generate ...

Abstract Background Although vaccination is one of the main countermeasures against influenza epidemic, it highly essential to make informed prevention decisions guarantee that limited resources are allocated places where they most needed. Hence, fundamental steps for decision making in characterize its spatio-temporal trend, especially on key problem about how transmits among adjacent and much...

There is a well-known Bayesian interpretation for function estimation by spline smoothing using a limit of proper normal priors. The limiting prior and the conditional and intrinsic autoregressive priors popular for spatial modelling have a common form, which we call partially informative normal. We derive necessary and sufficient conditions for the propriety of the posterior for this class of ...

In this paper we propose the Gaussian Dynamic Bayesian Smooth Transition Autoregressive (DBSTAR) models for nonlinear autoregressive time series processes as alternative to both the classical Smooth Transition Autoregressive (STAR) models of Chan and Tong (1986) and the computational Bayesian STAR (CBSTAR) models of Lopes and Salazar (2005). The DBSTAR models are autoregressive formulations of ...

This paper summarizes the literature on spatial filtering for analysis of spatial data, as proposed by Griffith (2000a). Given the scarcity of its application in transportation and its fledgling nature, preliminary case studies were conducted using continuous and discrete response data sets, for land values and land use, in comparison with results from spatial autoregressive models with distanc...