R-Vine Copula Mutual Information for Intermuscular Coupling Analysis

2021 ◽  
pp. 526-534
Author(s):  
Yating Wu ◽  
Qingshan She ◽  
Hongan Wang ◽  
Yuliang Ma ◽  
Mingxu Sun ◽  
...  
Keyword(s):  
Mutual Information ◽  
Coupling Analysis ◽  
Vine Copula

Vine copula selection using mutual information for hydrological dependence modeling

2020 ◽  
Vol 186 ◽  
pp. 109604 ◽  
Author(s):  
Lingling Ni ◽  
Dong Wang ◽  
Jianfeng Wu ◽  
Yuankun Wang ◽  
Yuwei Tao ◽  
...  
Keyword(s):  
Mutual Information ◽  
Dependence Modeling ◽  
Vine Copula

scholarly journals Coupling analysis of multivariate bioelectricity signal based symbolic partial mutual information

2013 ◽  
Vol 62 (6) ◽  
pp. 068704
Author(s):  
Zhang Mei ◽  
Cui Chao ◽  
Ma Qian-Li ◽  
Gan Zong-Liang ◽  
Wang Jun
Keyword(s):  
Mutual Information ◽  
Coupling Analysis

scholarly journals A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253307
Author(s):  
Charu Sharma ◽  
Niteesh Sahni
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Stock Returns ◽  
High Frequency ◽  
Value At Risk ◽  
Goodness Of Fit ◽  
Time Series Data ◽  
Series Data ◽  
Joint Probability Distribution ◽  
Vine Copula

In this paper, we explore mutual information based stock networks to build regular vine copula structure on high frequency log returns of stocks and use it for the estimation of Value at Risk (VaR) of a portfolio of stocks. Our model is a data driven model that learns from a high frequency time series data of log returns of top 50 stocks listed on the National Stock Exchange (NSE) in India for the year 2014. The Ljung-Box test revealed the presence of Autocorrelation as well as Heteroscedasticity in the underlying time series data. Analysing the goodness of fit of a number of variants of the GARCH model on each working day of the year 2014, that is, 229 days in all, it was observed that ARMA(1,1)-EGARCH(1,1) demonstrated the best fit. The joint probability distribution of the portfolio is computed by constructed an R-Vine copula structure on the data with the mutual information guided minimum spanning tree as the key building block. The joint PDF is then fed into the Monte-Carlo simulation procedure to compute the VaR. If we replace the mutual information by the Kendall’s Tau in the construction of the R-Vine copula structure, the resulting VaR estimations were found to be inferior suggesting the presence of non-linear relationships among stock returns.

SCCMI Algorithm for Coupling Analysis of Multi-Lead Epileptic EEG Signals

2013 ◽  
Vol 373-375 ◽  
pp. 681-684
Author(s):  
Chao Cui ◽  
Ting Ting Han ◽  
Meng Qiu Gao ◽  
Wei Lu ◽  
Chao Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Large Scale ◽  
Time Sequence ◽  
Eeg Signals ◽  
Coupling Analysis ◽  
Noise Effect ◽  
Code Algorithm ◽  
Symbolic Code ◽  
The Difference

Symbolic-code condition mutual information (SCCMI) algorithm is proposed,which can detect coupling between several systems.SCCMI combines condition mutual information with symbolic-code algorithm. Condition mutual information entropy is used to finding coupling degree between time series .The meaning of symbolic-code algorithm is to retention large scale information of time sequence, whats more ,reduce noise effect. SCCMI algorithm is used to analyze difference of coupling between epileptic EEG signals and normal ones .Hypothesis testing was done by SPSS.It turns out that the difference between epileptic EEG signals and normal ones is significant.SCCMI algorithm is proved to be effective. And coupling degree can be used as a parameter to measure if brain is healthy.

The maximum mutual information without coding for binary quantum-state signals

1998 ◽  
Vol 45 (2) ◽  
pp. 269-282 ◽  
Author(s):  
MASAO OSAKI, OSAMU HIROTA MASASHI BAN
Keyword(s):  
Mutual Information ◽  
Quantum State ◽  
Maximum Mutual Information

A mutual information‐based likelihood function for particle filter flood extent assimilation

2021 ◽  
Author(s):  
Antara Dasgupta ◽  
Renaud Hostache ◽  
RAAJ Ramasankaran ◽  
Guy J.‐P Schumann ◽  
Stefania Grimaldi ◽  
...  
Keyword(s):  
Mutual Information ◽  
Particle Filter ◽  
Likelihood Function

Nonlinear Pattern Analysis of Ventricular Premature Beats by Mutual Information

1997 ◽  
Vol 36 (04/05) ◽  
pp. 257-260 ◽  
Author(s):  
H. Saitoh ◽  
T. Yokoshima ◽  
H. Kishida ◽  
H. Hayakawa ◽  
R. J. Cohen ◽  
...  
Keyword(s):  
Mutual Information ◽  
Temporal Pattern ◽  
Pattern Analysis ◽  
Autonomic Activity ◽  
Correlation Measure ◽  
Ventricular Premature Beats ◽  
Risk Of Mortality ◽  
Premature Beats ◽  
Information Values ◽  
Nonlinear Pattern

Abstract:The frequency of ventricular premature beats (VPBs) has been related to the risk of mortality. However, little is known about the temporal pattern of occurrence of VPBs and its relationship to autonomic activity. Hence, we applied a general correlation measure, mutual information, to quantify how VPBs are generated over time. We also used mutual information to determine the correlation between VPB production and heart rate in order to evaluate effects of autonomic activity on VPB production. We examined twenty subjects with more than 3000 VPBs/day and simulated ran-( dom time series of VPB occurrence. We found that mutual information values could be used to characterize quantitatively the temporal patterns of VPB generation. Our data suggest that VPB production is not random and VPBs generated with a higher value of mutual information may be more greatly affected by autonomic activity.

Data Screening Methods – Application to Differential Diagnosis in Pancreatic Pathology from Radiological Signs

1978 ◽  
Vol 17 (01) ◽  
pp. 36-40 ◽  
Author(s):  
J.-P. Durbec ◽  
Jaqueline Cornée ◽  
P. Berthezene
Keyword(s):  
Differential Diagnosis ◽  
Mutual Information ◽  
Data Processing ◽  
Screening Methods ◽  
Automatic Data ◽  
Chronic Calcifying Pancreatitis ◽  
Number Of Patients ◽  
Calcifying Pancreatitis ◽  
Pancreatic Pathology ◽  
Data Screening

The practice of systematic examinations in hospitals and the increasing development of automatic data processing permits the storing of a great deal of information about a large number of patients belonging to different diagnosis groups.To predict or to characterize these diagnosis groups some descriptors are particularly useful, others carry no information. Data screening based on the properties of mutual information and on the log cross products ratios in contingency tables is developed. The most useful descriptors are selected. For each one the characterized groups are specified.This approach has been performed on a set of binary (presence—absence) radiological variables. Four diagnoses groups are concerned: cancer of pancreas, chronic calcifying pancreatitis, non-calcifying pancreatitis and probable pancreatitis. Only twenty of the three hundred and forty initial radiological variables are selected. The presence of each corresponding sign is associated with one or more diagnosis groups.

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