Search results for "Mathematica"

showing 10 items of 7971 documents

Wronskian and Casorati determinant representations for Darboux–Pöschl–Teller potentials and their difference extensions

2009

We consider some special reductions of generic Darboux?Crum dressing formulae and of their difference versions. As a matter of fact, we obtain some new formulae for Darboux?P?schl?Teller (DPT) potentials by means of Wronskian determinants. For their difference deformations (called DDPT-I and DDPT-II potentials) and the related eigenfunctions, we obtain new formulae described by the ratios of Casorati determinants given by the functional difference generalization of the Darboux?Crum dressing formula.

Statistics and ProbabilityAlgebraPure mathematicsNonlinear Sciences::Exactly Solvable and Integrable SystemsGeneralizationWronskianModeling and SimulationGeneral Physics and AstronomyStatistical and Nonlinear PhysicsEigenfunctionMathematical PhysicsMathematicsJournal of Physics A: Mathematical and Theoretical
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A Comparison of Formulae for Calculating Cost-Efficient Sample Sizes of Case-Control Studies with an Internal Validation Scheme

2000

When a case-control study is planned to include an internal validation study, the sample size of the study and the proportion of validated observations has to be calculated. There are a variety of alternative methods to accomplish this. In this article some possible procedures will be compared in order to clarify whether considerable differences in the suggested optimal designs occur, dependent on the used method.

Statistics and ProbabilityAlternative methodsScheme (programming language)Optimal designMathematical optimizationCost efficiencyEstimation theoryComputer scienceSmall sampleGeneral MedicineSample size determinationStatisticsStatistics Probability and UncertaintyInternal validationcomputercomputer.programming_languageBiometrical Journal
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ON THE ASYMPTOTIC DISTRIBUTION OF BARTLETT'S Up-STATISTIC

1985

Abstract. In this paper the asymptotic behaviour of Bartlett's Up-statistic for a goodness-of-fit test for stationary processes, is considered. The asymptotic distribution of the test process is given under the assumption that a central limit theorem for the empirical spectral distribution function holds. It is shown that the Up-statistic tends to the supremum of a tied down Brownian motion. By a counterexample we refute the conjecture that this distribution is in general of the Kolmogorov-Smirnov type. The validity of the central limit theorem for the spectral distribution function is then discussed. Finally a goodness-of-fit test for ARMA-processes based on the estimated innovation sequen…

Statistics and ProbabilityAnderson–Darling testApplied MathematicsMathematical analysisV-statisticAsymptotic distributionKolmogorov–Smirnov testEmpirical distribution functionsymbols.namesakeSampling distributionsymbolsTest statisticStatistics Probability and UncertaintyCentral limit theoremMathematicsJournal of Time Series Analysis
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On fractional diffusion and continuous time random walks

2003

Abstract A continuous time random walk model is presented with long-tailed waiting time density that approaches a Gaussian distribution in the continuum limit. This example shows that continuous time random walks with long time tails and diffusion equations with a fractional time derivative are in general not asymptotically equivalent.

Statistics and ProbabilityAnomalous diffusionGaussianMathematical analysisCondensed Matter PhysicsRandom walkFractional calculussymbols.namesakeDistribution (mathematics)Time derivativesymbolsLimit (mathematics)Continuous-time random walkMathematicsPhysica A: Statistical Mechanics and its Applications
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Using mathematical morphology for unsupervised classification of functional data

2011

This paper is concerned with the unsupervised classification of functional data by using mathematical morphology. Different morphological operators are used to extract relevant structures of the functions (considered as sets through their subgraph representations). These operators can be considered as preprocessing tools whose outputs are also functional data. We explore some dissimilarity measures and clustering methods for the classification of the transformed data. Our approach is illustrated through a detailed analysis of two data sets. These techniques, which have mainly been used in image processing, provide a flexible and robust toolbox for improving the results in unsupervised funct…

Statistics and ProbabilityApplied MathematicsData classificationImage processingMathematical morphologycomputer.software_genreToolboxComputingMethodologies_PATTERNRECOGNITIONModeling and SimulationPreprocessorData miningStatistics Probability and UncertaintyCluster analysisMorphological operatorscomputerMathematicsJournal of Statistical Computation and Simulation
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Weighted bounded mean oscillation applied to backward stochastic differential equations

2015

Abstract We deduce conditional L p -estimates for the variation of a solution of a BSDE. Both quadratic and sub-quadratic types of BSDEs are considered, and using the theory of weighted bounded mean oscillation we deduce new tail estimates for the solution ( Y , Z ) on subintervals of [ 0 , T ] . Some new results for the decoupling technique introduced in Geiss and Ylinen (2019) are obtained as well and some applications of the tail estimates are given.

Statistics and ProbabilityApplied MathematicsProbability (math.PR)010102 general mathematicsMathematical analysis01 natural sciencesBSDEsBounded mean oscillationdecoupling010104 statistics & probabilityStochastic differential equationvärähtelytQuadratic equationJohn-Nirenberg theoremtail estimatesModeling and Simulation60H10 60G99FOS: MathematicsDecoupling (probability)weighted bounded mean oscillation0101 mathematicsdifferentiaaliyhtälötMathematics - Probabilitystokastiset prosessitMathematicsStochastic Processes and their Applications
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Time-dependent weak rate of convergence for functions of generalized bounded variation

2016

Let $W$ denote the Brownian motion. For any exponentially bounded Borel function $g$ the function $u$ defined by $u(t,x)= \mathbb{E}[g(x{+}\sigma W_{T-t})]$ is the stochastic solution of the backward heat equation with terminal condition $g$. Let $u^n(t,x)$ denote the corresponding approximation generated by a simple symmetric random walk with time steps $2T/n$ and space steps $\pm \sigma \sqrt{T/n}$ where $\sigma > 0$. For quite irregular terminal conditions $g$ (bounded variation on compact intervals, locally H\"older continuous) the rate of convergence of $u^n(t,x)$ to $u(t,x)$ is considered, and also the behavior of the error $u^n(t,x)-u(t,x)$ as $t$ tends to $T$

Statistics and ProbabilityApproximation using simple random walkweak rate of convergence01 natural sciencesStochastic solution41A25 65M15 (Primary) 35K05 60G50 (Secondary)010104 statistics & probabilityExponential growthFOS: Mathematics0101 mathematicsBrownian motionstokastiset prosessitMathematicsosittaisdifferentiaaliyhtälötApplied MathematicsProbability (math.PR)010102 general mathematicsMathematical analysisfinite difference approximation of the heat equationFunction (mathematics)Rate of convergenceBounded functionBounded variationnumeerinen analyysiapproksimointiStatistics Probability and UncertaintyMathematics - ProbabilityStochastic Analysis and Applications
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Asymptotic optimality of myopic information-based strategies for Bayesian adaptive estimation

2016

This paper presents a general asymptotic theory of sequential Bayesian estimation giving results for the strongest, almost sure convergence. We show that under certain smoothness conditions on the probability model, the greedy information gain maximization algorithm for adaptive Bayesian estimation is asymptotically optimal in the sense that the determinant of the posterior covariance in a certain neighborhood of the true parameter value is asymptotically minimal. Using this result, we also obtain an asymptotic expression for the posterior entropy based on a novel definition of almost sure convergence on "most trials" (meaning that the convergence holds on a fraction of trials that converge…

Statistics and ProbabilityAsymptotic analysisMathematical optimizationPosterior probabilityBayesian probabilityMathematics - Statistics TheoryStatistics Theory (math.ST)050105 experimental psychologydifferential entropyDifferential entropyactive data selection03 medical and health sciences0302 clinical medicineactive learningFOS: Mathematics0501 psychology and cognitive sciencescost of observationdecision theoryMathematicsD-optimalityBayes estimatorSequential estimation05 social sciencesBayesian adaptive estimationAsymptotically optimal algorithmConvergence of random variablesasymptotic optimalitysequential estimation030217 neurology & neurosurgery
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On Independent Component Analysis with Stochastic Volatility Models

2017

Consider a multivariate time series where each component series is assumed to be a linear mixture of latent mutually independent stationary time series. Classical independent component analysis (ICA) tools, such as fastICA, are often used to extract latent series, but they don't utilize any information on temporal dependence. Also financial time series often have periods of low and high volatility. In such settings second order source separation methods, such as SOBI, fail. We review here some classical methods used for time series with stochastic volatility, and suggest modifications of them by proposing a family of vSOBI estimators. These estimators use different nonlinearity functions to…

Statistics and ProbabilityAutoregressive conditional heteroskedasticity01 natural sciencesQA273-280GARCH model010104 statistics & probabilityblind source separation0502 economics and businessSource separationEconometricsApplied mathematics0101 mathematics050205 econometrics MathematicsStochastic volatilitymultivariate time seriesApplied MathematicsStatistics05 social sciencesAutocorrelationEstimatorIndependent component analysisHA1-4737nonlinear autocorrelationFastICAStatistics Probability and UncertaintyVolatility (finance)Probabilities. Mathematical statistics
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Isotropic stochastic flow of homeomorphisms on Rd associated with the critical Sobolev exponent

2008

Abstract We consider the critical Sobolev isotropic Brownian flow in R d ( d ≥ 2 ) . On the basis of the work of LeJan and Raimond [Y. LeJan, O. Raimond, Integration of Brownian vector fields, Ann. Probab. 30 (2002) 826–873], we prove that the corresponding flow is a flow of homeomorphisms. As an application, we construct an explicit solution, which is also unique in a certain space, to the stochastic transport equation when the associated Gaussian vector fields are divergence free.

Statistics and ProbabilityBasis (linear algebra)Stochastic processApplied MathematicsMathematical analysisSpace (mathematics)Sobolev spaceStochastic differential equationMathematics::ProbabilityFlow (mathematics)Modeling and SimulationVector fieldBrownian motionMathematicsStochastic Processes and their Applications
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