Partial Directed Coherence and the Vector Autoregressive Modelling Myth and a Caveat

02/01/2022
by   Luiz Antonio Baccalá, et al.
0

Here we dispel the lingering myth that Partial Directed Coherence is a Vector Autoregressive (VAR) Modelling dependent concept. In fact, our examples show that it is spectral factorization that lies at its heart, for which VAR modelling is a mere, albeit very efficient and convenient, device. This applies to Granger Causality estimation procedures in general and also includes instantaneous Granger effects. Care, however, must be exercised for connectivity between multivariate data generated through nonminimum phase mechanisms as it may possibly be incorrectly captured.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/26/2020

An Explainable Model for EEG Seizure Detection based on Connectivity Features

Epilepsy which is characterized by seizures is studied using EEG signals...
research
12/07/2021

Testing for Causal Influence using a Partial Coherence Statistic

In this paper we explore partial coherence as a tool for evaluating caus...
research
12/31/2020

Towards Modelling Coherence in Spoken Discourse

While there has been significant progress towards modelling coherence in...
research
06/13/2023

Topological Data Analysis for Directed Dependence Networks of Multivariate Time Series Data

Topological data analysis (TDA) approaches are becoming increasingly pop...
research
03/31/2021

Spectral Dependence

This paper presents a general framework for modeling dependence in multi...
research
11/25/2022

Causal Vector Autoregression Enhanced with Covariance and Order Selection

A causal vector autoregressive (CVAR) model is introduced for weakly sta...
research
04/07/2023

ChiroDiff: Modelling chirographic data with Diffusion Models

Generative modelling over continuous-time geometric constructs, a.k.a su...

Please sign up or login with your details

Forgot password? Click here to reset