Autoregressive Models for Variance Matrices: Stationary Inverse Wishart Processes

Details Autoregressive Models for Variance Matrices: Stationary Inverse Wishart Processes Autoregressive Models for Variance Matrices: Stationary Inverse Wishart Processes

2011-07-26

arxiv.org/abs/1107.5239v1

Statistics

Methodology

Scientific paper

We introduce and explore a new class of stationary time series models for variance matrices based on a constructive definition exploiting inverse Wishart distribution theory. The main class of models explored is a novel class of stationary, first-order autoregressive (AR) processes on the cone of positive semi-definite matrices. Aspects of the theory and structure of these new models for multivariate "volatility" processes are described in detail and exemplified. We then develop approaches to model fitting via Bayesian simulation-based computations, creating a custom filtering method that relies on an efficient innovations sampler. An example is then provided in analysis of a multivariate electroencephalogram (EEG) time series in neurological studies. We conclude by discussing potential further developments of higher-order AR models and a number of connections with prior approaches.

Affiliated with

Also associated with

No associations

Rating

Autoregressive Models for Variance Matrices: Stationary Inverse Wishart Processes does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Autoregressive Models for Variance Matrices: Stationary Inverse Wishart Processes, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Autoregressive Models for Variance Matrices: Stationary Inverse Wishart Processes will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-93919