Search results for "Multivariate sign"

showing 5 items of 5 documents

Estimation of brain connectivity through Artificial Neural Networks

2019

Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved to be flexible and accurate. They rely on the solution of linear equations that can be pursued through artificial neural networks (ANNs) used as MVAR model. However, when few data samples are available, there is a lack of accuracy in estimating MVAR parameters due to the collinearity between regressors. Moreover, the assessment procedure is also affected by the lack of data points. The mathematical solution to these problems is represented by penalized regression methods based on l 1 norm, that can reduce collinearity by means of variable sel…

Computer scienceFeature selection02 engineering and technologyConnectivity measurements03 medical and health sciences0302 clinical medicine0202 electrical engineering electronic engineering information engineeringArtificial neural networkbusiness.industryProcess (computing)BrainPattern recognitionElectroencephalographyCollinearityCausalityData pointCausality; Connectivity measurements; Physiological systems modeling - Multivariate signal processingNorm (mathematics)Physiological systems modeling - Multivariate signal processingRegression Analysis020201 artificial intelligence & image processingAnalysis of varianceArtificial intelligenceNeural Networks ComputerbusinessAlgorithms Brain Electroencephalography Regression Analysis Neural Networks Computer030217 neurology & neurosurgeryLinear equationAlgorithms
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Sign and rank covariance matrices with applications to multivariate analysis

2002

asymptotic effciencyaffine equivariance multivariate analysisrobustnessmultivariate sign and rank
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The affine equivariant sign covariance matrix: asymptotic behavior and efficiencies

2003

We consider the affine equivariant sign covariance matrix (SCM) introduced by Visuri et al. (J. Statist. Plann. Inference 91 (2000) 557). The population SCM is shown to be proportional to the inverse of the regular covariance matrix. The eigenvectors and standardized eigenvalues of the covariance, matrix can thus be derived from the SCM. We also construct an estimate of the covariance and correlation matrix based on the SCM. The influence functions and limiting distributions of the SCM and its eigenvectors and eigenvalues are found. Limiting efficiencies are given in multivariate normal and t-distribution cases. The estimates are highly efficient in the multivariate normal case and perform …

Statistics and ProbabilityCovariance functionaffine equivarianceinfluence functionMultivariate normal distributionrobustnessComputer Science::Human-Computer InteractionEfficiencyestimatorsEstimation of covariance matricesScatter matrixStatisticsAffine equivarianceApplied mathematicsCMA-ESMultivariate signCovariance and correlation matricesRobustnessmultivariate medianMathematicsprincipal componentsInfluence functionNumerical AnalysisMultivariate medianCovariance matrixcovariance and correlation matricesdiscriminant-analysisCovarianceComputer Science::Otherdispersion matricesefficiencyLaw of total covariancemultivariate locationtestsStatistics Probability and Uncertaintyeigenvectors and eigenvaluesEigenvectors and eigenvaluesmultivariate signJournal of Multivariate Analysis
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On the Efficiency of Affine Invariant Multivariate Rank Tests

1998

AbstractIn this paper the asymptotic Pitman efficiencies of the affine invariant multivariate analogues of the rank tests based on the generalized median of Oja are considered. Formulae for asymptotic relative efficiencies are found and, under multivariate normal and multivariatetdistributions, relative efficiencies with respect to Hotelling'sT2test are calculated.

CombinatoricsStatistics and ProbabilityMultivariate statisticsNumerical AnalysisRank (linear algebra)Consistent estimatorAffine invariantStatistics::MethodologyMultivariate normal distributionStatistics Probability and UncertaintyAsymptotic efficiency Oja median multivariate signed-rank test multivariate-rank test Pitman efficiencyMathematicsJournal of Multivariate Analysis
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Single-trial Connectivity Estimation through the Least Absolute Shrinkage and Selection Operator.

2019

Methods based on the use of multivariate autoregressive models (MVAR) have proved to be an accurate tool for the estimation of functional links between the activity originated in different brain regions. A well-established method for the parameters estimation is the Ordinary Least Square (OLS) approach, followed by an assessment procedure that can be performed by means of Asymptotic Statistic (AS). However, the performances of both procedures are strongly influenced by the number of data samples available, thus limiting the conditions in which brain connectivity can be estimated. The aim of this paper is to introduce and test a regression method based on Least Absolute Shrinkage and Selecti…

Multivariate statisticsComputer science0206 medical engineering02 engineering and technologyConnectivity measurementsLeast squares03 medical and health sciences0302 clinical medicineLasso (statistics)Statistics::MethodologyLeast-Squares AnalysisStatisticShrinkagebusiness.industryBrainPattern recognitionElectroencephalography020601 biomedical engineeringCausalityData pointAutoregressive modelCausality; Connectivity measurements; Physiological systems modeling - Multivariate signal processingPhysiological systems modeling - Multivariate signal processingOrdinary least squaresLeast-Squares Analysis Brain ElectroencephalographyArtificial intelligencebusiness030217 neurology & neurosurgeryAnnual 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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