Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The recent interest in the dynamics of networks and the advent, across a range of applications, of measuring modalities that operate on different temporal scales have put the spotlight on some significant gaps in the theory of multivariate time series. Fundamental to the description of network dynamics is the direction of interaction beten nodes, accompanied by a measure of the strength of such interactions. Granger causality and its associated frequency domain strength measures GEMs due to Geke provide a framework for the formulation and analysis of these issues. In pursuing this setup, three significant unresolved issues emerge. First, computing GEMs involves computing submodels of vector time series models, for which reliable methods do not exist. Second, the impact of filtering on GEMs has never been definitively established. Third, the impact of downsampling on GEMs has never been established. In this work, using state-space methods,  resolve all these issues and illustrate the results with some simulations. Our analysis is motivated by some problems in fMRI brain imaging, to which  apply it, but it is of general applicability.
['Victor Solo']
I am writing a research paper on a idea State-space analysis of granger-geweke causality measures with application to fmri
[{"from": "human", "value": "I am writing a research paper on a idea State-space analysis of granger-geweke causality measures with application to fmri"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The recent interest in the dynamics of networks and the advent, across a range of applications, of measuring modalities that operate on different temporal scales have put the spotlight on some significant gaps in the theory of multivariate time series. Fundamental to the description of network dynamics is the direction of interaction beten nodes, accompanied by a measure of the strength of such interactions. Granger causality and its associated frequency domain strength measures GEMs due to Geke provide a framework for the formulation and analysis of these issues. In pursuing this setup, three significant unresolved issues emerge. First, computing GEMs involves computing submodels of vector time series models, for which reliable methods do not exist. Second, the impact of filtering on GEMs has never been definitively established. Third, the impact of downsampling on GEMs has never been established. In this work, using state-space methods,  resolve all these issues and illustrate the results with some simulations. Our analysis is motivated by some problems in fMRI brain imaging, to which  apply it, but it is of general applicability."}]