Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a twolayer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality in their time-varying variances (envelopes). The model is identified in two ste...

This article considers the quantile regression approach for partially linear spatial autoregressive models with possibly varying coefficients. B-spline is employed approximation of The instrumental variable parameter estimation. rank score tests are developed hypotheses on coefficients, including non-varying coefficients and constancy asymptotic properties proposed estimators test statistics bo...

In multivariate time series, the estimation of the covariance matrix of the observation innovations plays an important role in forecasting as it enables the computation of the standardized forecast error vectors as well as it enables the computation of confidence bounds of the forecasts. We develop an on-line, non-iterative Bayesian algorithm for estimation and forecasting. It is empirically fo...

Functional magnetic resonance imaging (fMRI) is widely used to identify neural correlates of cognitive tasks. However, the analysis of functional connectivity is crucial to understanding neural dynamics. Although many studies of cerebral circuitry have revealed adaptative behavior, which can change during the course of the experiment, most of contemporary connectivity studies are based on corre...

Time series and their methods of analysis are important subjects in statistics. Most of time series have a linear behavior and can be modelled by linear ARIMA models. However, some of realized time series have a nonlinear behavior and for modelling them one needs nonlinear models. For this, many good parametric nonlinear models such as bilinear model, exponential autoregressive model, threshold...

This paper considers modelling spatially varying regression effects for multivariate mortality count outcomes. Alternative approaches to spatial regression heterogeneity are considered: the multivariate normal conditional autoregressive (MCAR) model is contrasted with a flexible set of priors based on the multiple membership approach. These include spatial factor priors and a nonparametric appr...

9:55 – 10:40 Rodney Strachan (University of Queensland) Reducing Dimensions in Large Time-varying Parameter VAR Models This paper proposes a new approach to estimating high dimensional time varying parameter vector autoregressive models (TVP-VARs). Such models are rarely used with more than 4-5 variables. However recent work has shown the advantages of modelling VARs with large numbers of varia...

We report preliminary results of an eeort to use variants of the Hidden Markov Models developed by speech researchers to characterize persistence and recurrence of atmospheric circulation patterns in a 36 year record of Northern Hemisphere 700-mb geopotential heights. Using a cross validation scheme, we t autoregressive hidden Markov models (ARHMMs) with a range of complexities , varying the au...