Search results for "probability"

showing 10 items of 3417 documents

Embedding Quantum into Classical: Contextualization vs Conditionalization

2014

We compare two approaches to embedding joint distributions of random variables recorded under different conditions (such as spins of entangled particles for different settings) into the framework of classical, Kolmogorovian probability theory. In the contextualization approach each random variable is "automatically" labeled by all conditions under which it is recorded, and the random variables across a set of mutually exclusive conditions are probabilistically coupled (imposed a joint distribution upon). Analysis of all possible probabilistic couplings for a given set of random variables allows one to characterize various relations between their separate distributions (such as Bell-type ine…

Multivariate random variableFOS: Physical scienceslcsh:MedicineStability (probability)Joint probability distributionFOS: MathematicsMixture distributionStatistical physicslcsh:ScienceInverse distributionQuantum MechanicsProbabilityPhysicsta113Quantum PhysicsMultidisciplinaryModels StatisticalPhysicsProbability (math.PR)lcsh:RRandom Variables60A99 81P13Probability TheoryProbability DistributionAlgebra of random variablesEvents (Probability Theory)Sum of normally distributed random variablesPhysical SciencesQuantum Theorylcsh:QMarginal distributionQuantum EntanglementQuantum Physics (quant-ph)Mathematics - ProbabilityMathematicsResearch ArticlePlos One
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Influence Functions and Efficiencies of k-Step Hettmansperger–Randles Estimators for Multivariate Location and Regression

2016

In Hettmansperger and Randles (Biometrika 89:851–860, 2002) spatial sign vectors were used to derive simultaneous estimators of multivariate location and shape. Oja (Multivariate nonparametric methods with R. Springer, New York, 2010) proposed a similar approach for the multivariate linear regression case. These estimators are highly robust and have under general assumptions a joint limiting multinormal distribution. The estimates are easy to compute using fixed-point algorithms. There are however no exact proofs for the convergence of these algorithms. The existence and uniqueness of the solutions also still remain unproven although we believe that they hold under general conditions. To ci…

Multivariate statistics05 social sciencesNonparametric statisticsEstimator01 natural sciencesRegression010104 statistics & probabilityDistribution (mathematics)Bayesian multivariate linear regression0502 economics and businessLinear regressionEconometricsApplied mathematicsUniqueness0101 mathematics050205 econometrics Mathematics
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Modelling systemic price cojumps with Hawkes factor models

2015

Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating a set of 20 high cap stocks traded at the Italian Stock Exchange, we find that there is a large number of high frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets.

Multivariate statisticsEconomicsSystemic shockPoisson distribution01 natural sciencesSynchronizationEconometrics and Finance (all)2001 EconomicsFOS: Economics and business010104 statistics & probabilitysymbols.namesakeHigh frequency data0502 economics and businessEconomicsEconometricsCojumps0101 mathematicsCojumps; Hawkes processes; High frequency data; Systemic shocks; Finance; Economics Econometrics and Finance (all)2001 Economics Econometrics and Finance (miscellaneous)Time clusteringFactor analysisSettore SECS-S/06 - Metodi mat. dell'economia e Scienze Attuariali e FinanziarieStatistical Finance (q-fin.ST)050208 financeSystemic shocksHawkes processe05 social sciencesQuantitative Finance - Statistical FinanceEconomics Econometrics and Finance (all)2001 Economics Econometrics and Finance (miscellaneous)Econometrics and Finance (miscellaneous)symbolsCojumpHawkes processesGeneral Economics Econometrics and FinanceFinanceSign (mathematics)Quantitative Finance
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Testing Equality of Multiple Power Spectral Density Matrices

2018

This paper studies the existence of optimal invariant detectors for determining whether P multivariate processes have the same power spectral density. This problem finds application in multiple fields, including physical layer security and cognitive radio. For Gaussian observations, we prove that the optimal invariant detector, i.e., the uniformly most powerful invariant test, does not exist. Additionally, we consider the challenging case of close hypotheses, where we study the existence of the locally most powerful invariant test (LMPIT). The LMPIT is obtained in the closed form only for univariate signals. In the multivariate case, it is shown that the LMPIT does not exist. However, the c…

Multivariate statisticsGaussian02 engineering and technologyGeneralized likelihood tatio test (GLRT)Toeplitz matrixUniformly most powerful invariant test (UMPIT)01 natural sciencesElectronic mail010104 statistics & probabilitysymbols.namesakePower spectral density (PSD)0202 electrical engineering electronic engineering information engineeringApplied mathematics0101 mathematicsElectrical and Electronic EngineeringGeneralized likelihood ratio test (GLRT)MathematicsTelecomunicaciones1299 Otras Especialidades MatemáticasDetectorUnivariateSpectral density020206 networking & telecommunicationsInvariant (physics)Toeplitz matrixSignal ProcessingsymbolsTime-SeriesLocally most powerful invariant test (LMPIT)
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MuTE: a MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy.

2014

A challenge for physiologists and neuroscientists is to map information transfer between components of the systems that they study at different scales, in order to derive important knowledge on structure and function from the analysis of the recorded dynamics. The components of physiological networks often interact in a nonlinear way and through mechanisms which are in general not completely known. It is then safer that the method of choice for analyzing these interactions does not rely on any model or assumption on the nature of the data and their interactions. Transfer entropy has emerged as a powerful tool to quantify directed dynamical interactions. In this paper we compare different ap…

Multivariate statisticsInformation transferTheoretical computer scienceComputer scienceEntropyInformation TheorySocial SciencesCAUSALITYMedicine (all); Biochemistry Genetics and Molecular Biology (all); Agricultural and Biological Sciences (all)BioinformaticsMedicine and Health SciencesEntropy (energy dispersal)MultidisciplinaryEntropy (statistical thermodynamics)Medicine (all)QSoftware DevelopmentREstimatorSoftware EngineeringElectroencephalographyCausalityNeurologyCardiovascular DiseasesProbability distributionMedicineAlgorithmsResearch ArticleComputer ModelingComputer and Information SciencesScienceCardiologyProbability density functionEntropy (classical thermodynamics)Artificial IntelligenceLinear regressionEntropy (information theory)HumansComputer SimulationEntropy (arrow of time)Conditional entropyBiochemistry Genetics and Molecular Biology (all)EpilepsyBiology and Life SciencesModels TheoreticalMODELNonlinear systemAgricultural and Biological Sciences (all)ROC CurveINFORMATION-TRANSFERSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaCognitive ScienceTransfer entropySoftwareEntropy (order and disorder)NeurosciencePLoS ONE
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Second-order interaction in a Trivariate Generalized Gamma Distribution

2004

The concept of second- (and higher-) order interaction is widely used in categorical data analysis, where it proves useful for explaining the interdependence among three (or more) variables. Its use seems to be less common for continuous multivariate distributions, most likely owing to the predominant role of the Multivariate Normal distribution, for which any interaction involving more than two variables is necessarily zero. In this paper we explore the usefulness of a second-order interaction measure for studying the interdependence among three continuous random variables, by applying it to a trivariate Generalized Gamma distribution proposed by Bologna(2000).

Multivariate statisticsInteractionJoint probability distributionStatisticsGeneralized gamma distributionGeneralized integer gamma distributionMultivariate normal distributionStatisticalClassificationRandom variableMeasure (mathematics)Zero (linguistics)Mathematics
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Forecasting correlated time series with exponential smoothing models

2011

Abstract This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters’ model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection crite…

Multivariate statisticsMathematical optimizationsymbols.namesakeModel selectionExponential smoothingPosterior probabilitysymbolsUnivariateMarkov chain Monte CarloBusiness and International ManagementSeemingly unrelated regressionsBayesian inferenceMathematicsInternational Journal of Forecasting
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Prediction of chromatographic properties of organophosphorus insecticides by molecular connectivity

2000

A study is reported of the relationship between theR F values for a group of organophosphorus insecticides obtained by thin layer chromatography and a series of topological descriptors. By using multivariate regression, the corresponding connectivity functions were obtained, which had been selected on the basis of their respective statistical parameters: multiple correlation coefficient (r), standard error of estimate (s), F-Snedecor values and statistical significance (Student’s t). Regression analysis of the connectivity functions can predict the elution behaviour of any structurally similar derivative of this group of compounds with different stationary and mobile phases. Stability studi…

Multivariate statisticsQuantitative structure–activity relationshipChromatographyElutionChemistryOrganic ChemistryClinical BiochemistryStatistical parameterRegression analysisDerivativeBiochemistryStability (probability)Analytical ChemistryMultiple correlation
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Locally optimal invariant detector for testing equality of two power spectral densities

2018

This work addresses the problem of determining whether two multivariate random time series have the same power spectral density (PSD), which has applications, for instance, in physical-layer security and cognitive radio. Remarkably, existing detectors for this problem do not usually provide any kind of optimality. Thus, we study here the existence under the Gaussian assumption of optimal invariant detectors for this problem, proving that the uniformly most powerful invariant test (UMPIT) does not exist. Thus, focusing on close hypotheses, we show that the locally most powerful invariant test (LMPIT) only exists for univariate time series. In the multivariate case, we prove that the LMPIT do…

Multivariate statisticsSeries (mathematics)Computer scienceGaussianDetectorUnivariateSpectral density020206 networking & telecommunications02 engineering and technologyUniformly most powerful invariant test (UMPIT)01 natural sciencesMatrix decomposition010104 statistics & probabilitysymbols.namesakePower spectral density (PSD)0202 electrical engineering electronic engineering information engineeringsymbols0101 mathematicsInvariant (mathematics)Time seriesHypothesis testGeneralized likelihood ratio test (GLRT)AlgorithmLocally most powerful invariant test (LMPIT)Statistical hypothesis testing
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Non-Parametric Rank Statistics for Spectral Power and Coherence

2019

AbstractDespite advances in multivariate spectral analysis of neural signals, the statistical inference of measures such as spectral power and coherence in practical and real-life scenarios remains a challenge. The non-normal distribution of the neural signals and presence of artefactual components make it difficult to use the parametric methods for robust estimation of measures or to infer the presence of specific spectral components above the chance level. Furthermore, the bias of the coherence measures and their complex statistical distributions are impediments in robust statistical comparisons between 2 different levels of coherence. Non-parametric methods based on the median of auto-/c…

Multivariate statisticsbusiness.industryComputer scienceStatistical inferenceNonparametric statisticsProbability distributionCoherence (signal processing)Spectral analysisDigital signalPattern recognitionArtificial intelligencebusinessCoherence (physics)
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