New Insights into Signed Path Coefficient Granger Causality Analysis [PDF]
Frontiers in Neuroinformatics, 2016 Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on
Jian Zhang +3 more
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Robust unified Granger causality analysis: a normalized maximum likelihood form [PDF]
Brain Informatics, 2021 Unified Granger causality analysis (uGCA) alters conventional two-stage Granger causality analysis into a unified code-length guided framework. We have presented several forms of uGCA methods to investigate causal connectivities, and different forms of ...
Zhenghui Hu +5 more
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Description length guided nonlinear unified Granger causality analysis [PDF]
Harvard Data Science Review, 2023 Abstract Most Granger causality analysis (GCA) methods still remain a two-stage scheme guided by different mathematical theories; both can actually be viewed as the same generalized model selection issues. Adhering to Occam’s razor, we present a unified GCA (uGCA) based on the minimum description length principle.
Fei Li +3 more
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Description Length Guided Unified Granger Causality Analysis [PDF]
IEEE Access, 2021 In this article, we propose a description length guided unified Granger causality analysis (uGCA) framework for sequential medical imaging. While existing efforts of GCA focused on causal relation design and statistical methods for their improvement, our
Zhenghui Hu +3 more
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Depressed MEG causality analysis based on polynomial kernel Granger causality
AIP Advances, 2023 In this study, we employ the Granger causality of a polynomial kernel to identify the coupling causality of depressed magnetoencephalography (MEG). We collect MEG under positive, neutral, and negative emotional stimuli and focus on the β-band activities.
Jing Qian +7 more
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Extracting neuronal functional network dynamics via adaptive Granger causality analysis. [PDF]
Proc Natl Acad Sci U S A, 2018 Significance Probing functional interactions among the nodes in a network is crucial to understanding how complex systems work. Existing methodologies widely assume static network structures or Gaussian statistics or do not take account of likely sparse interactions.
Sheikhattar A +6 more
europepmc +5 more sources
The Relationships among Cryptocurrencies: A Granger Causality Analysis
iRASD Journal of Economics, 2022 The topic of Cryptocurrencies is emerging rapidly. Many investors and public want to invest in digital currencies. So, the nature of cryptocurrency and its theoretical understanding is essential currently.
Nadir Khan +4 more
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Schizophrenia MEG Network Analysis Based on Kernel Granger Causality
Entropy, 2023 Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct ...
Qiong Wang +5 more
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Automated diagnosis of HIV-associated neurocognitive disorders using large-scale Granger causality analysis of resting-state functional MRI. [PDF]
Comput Biol Med, 2019 HIV-associated neurocognitive disorders (HAND) represent an important source of neurologic complications in individuals with HIV. The dynamic, often subclinical, course of HAND has rendered diagnosis, which currently depends on neuropsychometric (NP ...
Chockanathan U +4 more
europepmc +2 more sources
Video Sensor-Based Complex Scene Analysis with Granger Causality [PDF]
Sensors, 2013 In this report, we propose a novel framework to explore the activity interactions and temporal dependencies between activities in complex video surveillance scenes.
Shuang Wu +4 more
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