Search results for "Grange"

showing 10 items of 164 documents

Information Dynamics Analysis: A new approach based on Sparse Identification of Linear Parametric Models*

2020

The framework of information dynamics allows to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of a complex network. The information transfer from one process to another can be quantified through Transfer Entropy, and under the assumption of joint Gaussian variables it is strictly related to the concept of Granger Causality (GC). According to the most recent developments in the field, the computation of GC entails representing the processes through a Vector Autoregressive (VAR) model and a state space (SS) model typically identified by means of the Ordinary Least Squares (OLS). In this work, we propose a new identification …

Multivariate statisticsComputer scienceEntropyGaussian0206 medical engineeringNormal Distribution02 engineering and technology01 natural sciencesLASSO regression010305 fluids & plasmassymbols.namesakeinformation TransferState Space modelsGranger causalityLasso (statistics)0103 physical sciencesStatistics::MethodologyState spaceLeast-Squares AnalysisShrinkageSparse matrixElectroencephalography020601 biomedical engineeringinformation Transfer; LASSO regression; State Space models; Granger causalityAutoregressive modelstate space modelParametric modelOrdinary least squaresLinear ModelssymbolsGranger causalityTransfer entropyAlgorithmInformation dyancamic analysi
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Decomposing the transfer entropy to quantify lag-specific Granger causality in cardiovascular variability.

2013

We present a modification of the well known transfer entropy (TE) which makes it able to detect, besides the direction and strength of the information transfer between coupled processes, its exact timing. The approach follows a decomposition strategy which identifies--according to a lag-specific formulation of the concept of Granger causality--the set of time delays carrying significant information, and then assigns to each of these delays an amount of information transfer such that the total contribution yields the overall TE. We propose also a procedure for the practical estimation from time series data of the relevant delays and lag-specific TE in both bivariate and multivariate settings…

Multivariate statisticsMathematical optimizationInformation transferMedicine (all)LagEntropyBivariate analysisCardiovascular Physiological PhenomenaGranger causalitySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaMultivariate AnalysisEntropy (information theory)HumansTransfer entropyComputer SimulationTime seriesAlgorithmsMathematicsAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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Algorithms for the inference of causality in dynamic processes: Application to cardiovascular and cerebrovascular variability

2015

This study faces the problem of causal inference in multivariate dynamic processes, with specific regard to the detection of instantaneous and time-lagged directed interactions. We point out the limitations of the traditional Granger causality analysis, showing that it leads to false detection of causality when instantaneous and time-lagged effects coexist in the process structure. Then, we propose an improved algorithm for causal inference that combines the Granger framework with the approach proposed by Pearl for the study of causality among multiple random variables. This new approach is compared with the traditional one in theoretical and simulated examples of interacting processes, sho…

Multivariate statisticsProcess (engineering)Computer scienceBiomedical EngineeringInferenceHealth InformaticsMachine learningcomputer.software_genreHeart RateEconometricsHumansArterial PressureComputer Simulation1707Granger causality analysisSeries (mathematics)business.industryBrainHeartCausalityCausalityCerebrovascular CirculationCausal inferenceSignal ProcessingSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaArtificial intelligencebusinesscomputerRandom variableAlgorithms2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series

2012

The complexity of the short-term cardiovascular control prompts for the introduction of multivariate (MV) nonlinear time series analysis methods to assess directional interactions reflecting the underlying regulatory mechanisms. This study introduces a new approach for the detection of nonlinear Granger causality in MV time series, based on embedding the series by a sequential, non-uniform procedure, and on estimating the information flow from one series to another by means of the corrected conditional entropy. The approach is validated on short realizations of linear stochastic and nonlinear deterministic processes, and then evaluated on heart period, systolic arterial pressure and respira…

Multivariate statisticsSupine positionMultivariate analysisQuantitative Biology::Tissues and OrgansTime delay embeddingPhysics::Medical PhysicsPostureBlood PressureHealth InformaticsCardiovascular Physiological PhenomenaGranger causalityPosition (vector)StatisticsHumansCardiovascular interactionMathematicsConditional entropySeries (mathematics)RespirationModels CardiovascularReproducibility of ResultsSignal Processing Computer-AssistedComputer Science Applications1707 Computer Vision and Pattern RecognitionComputer Science ApplicationsNonlinear systemNonlinear DynamicsMultivariate AnalysisSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityMultivariate time serieConditional entropyAlgorithmAlgorithms
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Measuring frequency domain granger causality for multiple blocks of interacting time series

2011

In the past years, several frequency-domain causality measures based on vector autoregressive time series modeling have been suggested to assess directional connectivity in neural systems. The most followed approaches are based on representing the considered set of multiple time series as a realization of two or three vector-valued processes, yielding the so-called Geweke linear feedback measures, or as a realization of multiple scalar-valued processes, yielding popular measures like the directed coherence (DC) and the partial DC (PDC). In the present study, these two approaches are unified and generalized by proposing novel frequency-domain causality measures which extend the existing meas…

Multivariate statisticsTime FactorsGeneral Computer ScienceLogarithmScalar (mathematics)Complex systemTopologyModels BiologicalNeurophysiological time serieBlock-based connectivity analysiGranger causalityStatisticsHumansComputer SimulationDirected coherenceMathematicsNumerical analysisPartial directed coherenceBrainElectroencephalographyVector autoregressive (VAR) modelBrain WavesCausalityAutoregressive modelFrequency domainComputer ScienceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityAlgorithmsBiotechnologyBiological Cybernetics
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Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions.

2010

The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. M…

Multivariate statisticsTime FactorsGeneral Computer ScienceModels NeurologicalPattern Recognition AutomatedCardiovascular Physiological PhenomenaElectrocardiographyGranger causalityArtificial IntelligenceEconometricsCoherence (signal processing)AnimalsHumansComputer SimulationEEGPartial Directed CoherenceMathematicsCausal modelMultivariate autoregressive modelComputer Science (all)Linear modelElectroencephalographySignal Processing Computer-AssistedCardiovascular variabilityAutoregressive modelFrequency domainParametric modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityMultivariate time serieLinear ModelsNeural Networks ComputerBiotechnologyBiological cybernetics
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Testing different methodologies for Granger causality estimation: A simulation study

2021

Granger causality (GC) is a method for determining whether and how two time series exert causal influences one over the other. As it is easy to implement through vector autoregressive (VAR) models and can be generalized to the multivariate case, GC has spread in many different areas of research such as neuroscience and network physiology. In its basic formulation, the computation of GC involves two different regressions, taking respectively into account the whole past history of the investigated multivariate time series (full model) and the past of all time series except the putatively causal time series (restricted model). However, the restricted model cannot be represented through a finit…

Multivariate statisticsstate space modelsSeries (mathematics)Computer scienceGranger causality; state space modelsDynamical NetworksMultivariate Time SeriesReduction (complexity)Autoregressive modelGranger causalitySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityState spaceConditioningTime seriesVector Autoregressive ProcessesAlgorithm2020 28th European Signal Processing Conference (EUSIPCO)
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Astronomiskās un dabas parādības aizvēsturē Ziemeļeiropā: atspoguļojums vēstures avotos un arheoloģiskajā materiālā

2021

Cilvēka dzīve aizvēsturē bija ļoti cieši saista ar dabu. Līdz ar pāreju uz ražotājsaimniecību vēlā akmens laikmeta beigās, bet īpaši sākot ar bronzas laikmetu, izveidojās ideoloģija, kurā centrālo vietu ieņēma Saule un ar to saistīto gadskārtu cikli. Solārā un lunārā simbolika plaši pārstāvēta Ziemeļeiropas, arī Latvijas arheoloģiskajā materiālā. Dabas un astronomiskās parādības minētas arī pirmajos rakstītajos avotos, bet to sekas – īpaši dabas katastrofu sekas, fiksējamas arī arheoloģiskajā materiālā.

Nebras debesu disks. Saksija Anhalte Vācija (1800 pr. Kr.)Virgas Kalnazīverti. 4.gs.Lielā 1264. gada komētaVikingu laikmets (793. – 1066.g.):HUMANITIES and RELIGION::History and philosophy subjects::History subjects::History [Research Subject Categories]Vēvelsburga (Wewelsburg) VācijaVilkumuižas ezersĀraišu ezerpils (9. – 10.gs.):HUMANITIES and RELIGION::History and philosophy subjects::Historical cultures::Classical archaeology and ancient history [Research Subject Categories]Septiņu sauļu (Siebenfältiges Sonnenwunder ) motīvsLielie Greizie ratiAlemāņu sakta. 7.gs.Ņūgrendža (Newgrange) Īrija (3200.g.pr. Kr.)Klimata izmaiņas bronzas laikmetāMazais ledus laikmetsSaules rati Trundholma Dānija (1400.pr. Kr.):HUMANITIES and RELIGION::History and philosophy subjects::Archaeology subjects::Archaeology [Research Subject Categories]
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Fingerprints of heavy scales in electroweak effective Lagrangians

2017

The couplings of the electroweak effective theory contain information on the heavy-mass scales which are no-longer present in the low-energy Lagrangian. We build a general effective Lagrangian, implementing the electroweak chiral symmetry breaking $SU(2)_L\otimes SU(2)_R\to SU(2)_{L+R}$, which couples the known particle fields to heavier states with bosonic quantum numbers $J^P=0^\pm$ and $1^\pm$. We consider colour-singlet heavy fields that are in singlet or triplet representations of the electroweak group. Integrating out these heavy scales, we analyze the pattern of low-energy couplings among the light fields which are generated by the massive states. We adopt a generic non-linear realiz…

Nuclear and High Energy PhysicsParticle physicsFísica-Modelos matemáticosHiggs PhysicsFOS: Physical sciences01 natural sciencesHigh Energy Physics - Phenomenology (hep-ph)0103 physical sciencesEffective field theoryFísica matemáticaPartículas (Física nuclear)lcsh:Nuclear and particle physics. Atomic energy. RadioactivityElectromagnetismoSymmetry breakingSinglet state010306 general physicsParticles (Nuclear physics)Huellas dactilares.PhysicsQuantum chromodynamics010308 nuclear & particles physicsHigh Energy Physics::PhenomenologyElectroweak interactionCromodinámica cuántica.Technicolor and Composite ModelsQuantum numberLagrangian functions.High Energy Physics - PhenomenologyFingerprints.Simetría (Física)Beyond Standard ModelChiral LagrangiansQuantum chromodynamics.Higgs bosonlcsh:QC770-798Chiral symmetry breakingSymmetry (Physics)Lagrange Funciones de.Journal of High Energy Physics
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Global analysis of fragmentation functions for eta mesons

2011

Fragmentation functions for eta mesons are extracted at next-to-leading order accuracy of QCD in a global analysis of data taken in electron-positron annihilation and proton-proton scattering experiments. The obtained parametrization is in good agreement with all data sets analyzed and can be utilized, for instance, in future studies of double-spin asymmetries for single-inclusive eta production. The Lagrange multiplier technique is used to estimate the uncertainties of the fragmentation functions and to assess the role of the different data sets in constraining them.

Nuclear and High Energy PhysicsParticle physicsMesonCiencias FísicasElectron–positron annihilationHadronFOS: Physical sciencesElementary particle//purl.org/becyt/ford/1 [https]symbols.namesakeHigh Energy Physics - Phenomenology (hep-ph)Fragmentation FunctionsNuclear ExperimentQuantum chromodynamicsPhysicsAnnihilation//purl.org/becyt/ford/1.3 [https]QcdEtaAstronomíaHigh Energy Physics - PhenomenologyLagrange multiplierData analysissymbolsHigh Energy Physics::ExperimentCIENCIAS NATURALES Y EXACTASHadronization
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