Search results for "multivariate statistics"

showing 10 items of 290 documents

Multivariate geophysical survey to detect a shallow fault zone in a landfill project area

2018

An integrated analysis of 2D high-resolution shallow seismic refraction tomographies (SRT) and electrical resistivity tomographies (ERT) has been carried out along a slope where the presence of a fault zone was assumed. It was also applied a post-inversion k-means cluster analysis of the P-wave velocity, the density of the seismic rays and the electrical resistivity of the interpretation models. Distribution maps of the cluster in multi-space were built, allowing to better definethe lateral geometry of a NE-SW directed band composed of intensely tectonized carbonate breccias. Finally, the fracturing and kinematic analysis on fault planes observed along the trenches, highlighted systems of l…

Multivariate statisticsSettore GEO/02 - Geologia Stratigrafica E SedimentologicaSettore GEO/11 - Geofisica ApplicataGeophysical surveyProject areaSeimic refraction tomography Electrical resistivity tomography Cluster Analysis Fault BellolampoGeologySeismology
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Determination of vinegar acidity by attenuated total reflectance infrared measurements through the use of second-order absorbance-pH matrices and par…

2007

Univariate (zero-order), multivariate (first-order) and multiway (second-order) calibrations were assayed for the determination of vinegar acidity using a mechanized procedure based upon vibrational spectroscopy and the emerging multicommutation methodology. The second-order methodology relies on the use of a flow system based on multicommutation and binary sampling. The flow network comprises a set of three-way solenoid valves, computer-controlled to provide facilities to handle the sample and to generate a time-dependent pH gradient using two carrier solutions. The procedure is based on the volumetric fraction variation approach that maintains the same volume of sample solution and dynami…

Multivariate statisticsSpectrophotometry InfraredChemistryAnalytical chemistrySampling (statistics)Hydrogen-Ion ConcentrationAnalytical ChemistryChemometricsAbsorbanceAttenuated total reflectionPartial least squares regressionCalibrationTitrationFactor Analysis StatisticalAcetic AcidTalanta
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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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Multivariate and Multiscale Complexity of Long-Range Correlated Cardiovascular and Respiratory Variability Series

2020

Assessing the dynamical complexity of biological time series represents an important topic with potential applications ranging from the characterization of physiological states and pathological conditions to the calculation of diagnostic parameters. In particular, cardiovascular time series exhibit a variability produced by different physiological control mechanisms coupled with each other, which take into account several variables and operate across multiple time scales that result in the coexistence of short term dynamics and long-range correlations. The most widely employed technique to evaluate the dynamical complexity of a time series at different time scales, the so-called multiscale …

Multivariate statisticsSystolic arterial pressure (SAP)Vector autoregressive fractionally integrated (VARFI) modelsComputer scienceGeneral Physics and Astronomylcsh:Astrophysics01 natural sciencesArticle010305 fluids & plasmaslcsh:QB460-4660103 physical sciencesRange (statistics)Multi-scale entropy (MSE)lcsh:Science010306 general physicsRepresentation (mathematics)Parametric statisticsvector autoregressive fractionally integrated (VARFI) modelSeries (mathematics)multi-scale entropy (MSE)Stochastic processsystolic arterial pressure (SAP)lcsh:QC1-999Term (time)Autoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E Informaticavector autoregressive fractionally integrated (VARFI) modelslcsh:QBiological systemHeart rate variability (HRV)lcsh:Physicsheart rate variability (HRV)
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Incorporating classified dispersal assumptions in predictive distribution models – A case study with grasshoppers and bush-crickets

2011

Abstract Current and future species distributions depend on environmental conditions, but the ability of species to shift their range boundaries or to expand their distribution ranges in response to global change also depends on their dispersal capacity. Dispersal capacity, however, has often been neglected in previous studies that either assumed no-dispersal or full dispersal, both of which are unrealistic for most taxa. The aims of this study are (i) to identify the predictors of the present spatial distribution on a regional scale for 13 grasshoppers and bush-crickets, and (ii) to derive predictions of their future distributions under climate change by applying different dispersal capaci…

Multivariate statisticsTaxonEcologyRange (biology)Ecological ModelingSpecies distributionBiological dispersalClimate changeGlobal changeBiologySpatial distributionEcological Modelling
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Multivariate correlation measures reveal structure and strength of brain–body physiological networks at rest and during mental stress

2021

In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of delta, theta, alpha, and beta electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability. MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain–body interaction…

Multivariate statisticsTechnology and EngineeringElectroencephalographybrain-heart connectionNetwork topologynetwork physiologylcsh:RC321-571Correlation03 medical and health sciences0302 clinical medicinewearable devicesMedicine and Health SciencesmedicineMultiple correlationSubnetworklcsh:Neurosciences. Biological psychiatry. Neuropsychiatryinformation theory030304 developmental biologyMathematicsOriginal Researchphysiological stressbrain-body interactionsNetwork physiology brain–heart connection cardiovascular oscillations EEG waves physiological stress time series analysis wearable devices0303 health sciencesnetwork physiology; brain-heart connection; cardiovascular oscillations; EEG waves; physiological stressmedicine.diagnostic_testPulse (signal processing)General NeuroscienceCardiorespiratory fitnessbrain–heart connectionMathematics and Statisticscardiovascular oscillationsnetworkstime series analysisphysiologySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaNeuroscience030217 neurology & neurosurgeryEEG wavesNeuroscience
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Detecting nonlinear causal interactions between dynamical systems by non-uniform embedding of multiple time series.

2010

This study introduces a new approach for the detection of nonlinear Granger causality between dynamical systems. The approach is based on embedding the multivariate (MV) time series measured from the systems X and Y by means of a sequential, non-uniform procedure, and on using the corrected conditional entropy (CCE) as unpredictability measure. The causal coupling from X to Y is quantified as the relative decrease of CCE measured after allowing the series of X to enter the embedding procedure for the description of Y. The ability of the approach to quantify nonlinear causality is assessed on MV time series measured from simulated dynamical systems with unidirectional coupling (the Rössler-…

Multivariate statisticsTime FactorsDynamical systems theoryEntropyBiomedical EngineeringMachine learningcomputer.software_genreHumansStatistical physicsTime seriesMathematicsVisual CortexConditional entropyCouplingSignal processingbusiness.industryMagnetoencephalographyReproducibility of ResultsSignal Processing Computer-AssistedSomatosensory CortexNonlinear systemNonlinear DynamicsSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaMultivariate AnalysisEmbeddingArtificial intelligencebusinesscomputer
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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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Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping

2018

Abstract Gully erosion has been identified as an important soil degradation process and sediment source, especially in arid and semiarid areas. Thus, it is useful to identify the spatial occurrence of this form of water erosion in the landscape and the most vulnerable areas. In this study, we explored the effects of different pixel sizes on some controlling factors extracted from a digital elevation model and remote sensing data when producing a gully erosion susceptibility map (GESM) of Ekbatan Dam Basin, Hamadan, Iran. An inventory map of the gully landforms was prepared based on global positioning system routes of the gullies, extensive field surveys, and visual interpretations of satell…

Multivariate statisticsTopographic Wetness IndexRemote sensing data010504 meteorology & atmospheric sciencesPixelTopographic attributeSettore GEO/04 - Geografia Fisica E Geomorfologia0208 environmental biotechnologySoil Science02 engineering and technology01 natural sciencesNormalized Difference Vegetation Index020801 environmental engineeringData setGully erosionMachine learning modelSoil retrogression and degradationRobustneEnvironmental scienceDigital elevation model0105 earth and related environmental sciencesRemote sensingStatistical hypothesis testingGeoderma
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