Functional MRI and multivariate autoregressive models

Chapter 40 : Multivariate autoregressive models

The development of functional and effective connectivity for narrative processing in children investigated via functional MRI and Multivariate Autoregressive Modeling

V. J. Schmithorst, S. K. Holland Imaging Research Center, Children's Hospital Medical Center, Cincinnati, OH, United States Introduction During the early school years, school performance is significantly impacted by story comprehension skills [1]. To investigate the neural correlates of auditory sentence processing and narrative comprehension in children, we performed a large-scale study using ...

Gaussian Processes for Functional-Coefficient Autoregressive Models

This work is concerned with nonlinear time series models and, in particular, with nonparametric models for the dynamics of the mean of the time series. We build on the functional-coefficient autoregressive (FAR) model of Chen and Tsay (1993) which is a generalization of the autoregressive (AR) model where the coefficients are varying and are given by functions of the lagged values of the series...

Multivariate Autoregressive Mixture Models for Music Auto-Tagging

We propose the multivariate autoregressive model for content based music auto-tagging. At the song level our approach leverages the multivariate autoregressive mixture (ARM) model, a generative time-series model for audio, which assumes each feature vector in an audio fragment is a linear function of previous feature vectors. To tackle tagmodel estimation, we propose an efficient hierarchical E...

Estimation for Partially Nonstationary Multivariate Autoregressive Models with Conditional Heteroskedasticity

where $‘s inre r*onst,ant matricaes; detI{@(z)} = 11 @,x . w * $JP[ = 0 has ci 5 771, urrit roots and ‘I’ = 711 d roots omside the urrit, circle: tPt = ((1 it, 7 c+> is a sequcnce of independent1 and idcntically distlributled (i.i.tl) matrices with mean zero and nonnegativc covarianc~e IC[ /le+&) ~f’(&)] = 0; pit is an i.i.d ramlom vector witIh mean zero and positive covariance E ( etef j = CA ...