Search results for "Multivariate t-distribution"

showing 3 items of 3 documents

Multivariate exponential smoothing: A Bayesian forecast approach based on simulation

2009

This paper deals with the prediction of time series with correlated errors at each time point using a Bayesian forecast approach based on the multivariate Holt-Winters model. Assuming that each of the univariate time series comes from the univariate Holt-Winters model, all of them sharing a common structure, the multivariate Holt-Winters model can be formulated as a traditional multivariate regression model. This formulation facilitates obtaining the posterior distribution of the model parameters, which is not analytically tractable: simulation is needed. An acceptance sampling procedure is used in order to obtain a sample from this posterior distribution. Using Monte Carlo integration the …

Numerical AnalysisMultivariate statisticsGeneral Computer ScienceApplied MathematicsUnivariateMarkov chain Monte CarloTheoretical Computer ScienceNormal-Wishart distributionsymbols.namesakeUnivariate distributionModeling and SimulationStatisticssymbolsMultivariate t-distributionBayesian linear regressionGibbs samplingMathematicsMathematics and Computers in Simulation
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Spectral density of the correlation matrix of factor models: a random matrix theory approach.

2005

We studied the eigenvalue spectral density of the correlation matrix of factor models of multivariate time series. By making use of the random matrix theory, we analytically quantified the effect of statistical uncertainty on the spectral density due to the finiteness of the sample. We considered a broad range of models, ranging from one-factor models to hierarchical multifactor models.

CombinatoricsScatter matrixCentering matrixMatrix functionStatistical physicsMultivariate t-distributionNonnegative matrixFinance Commerce correlation matrixRandom matrixSquare matrixData matrix (multivariate statistics)MathematicsPhysical review. E, Statistical, nonlinear, and soft matter physics
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Affine equivariant multivariate rank methods

2003

The classical multivariate statistical methods (MANOVA, principal component analysis, multivariate multiple regression, canonical correlation, factor analysis, etc.) assume that the data come from a multivariate normal distribution and the derivations are based on the sample covariance matrix. The conventional sample covariance matrix and consequently the standard multivariate techniques based on it are, however, highly sensitive to outlying observations. In the paper a new, more robust and highly efficient, approach based on an affine equivariant rank covariance matrix is proposed and outlined. Affine equivariant multivariate rank concept is based on the multivariate Oja (Statist. Probab. …

Statistics and ProbabilityPure mathematicsApplied MathematicsMatrix t-distributionMultivariate normal distributionNormal-Wishart distributionCombinatoricsEstimation of covariance matricesScatter matrixStatistics::MethodologyMatrix normal distributionMultivariate t-distributionStatistics Probability and UncertaintyMathematicsMultivariate stable distributionJournal of Statistical Planning and Inference
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