Search results for " Asymptotic normality"

showing 3 items of 3 documents

Topology-based goodness-of-fit tests for sliced spatial data

2023

In materials science and many other application domains, 3D information can often only be extrapolated by taking 2D slices. In topological data analysis, persistence vineyards have emerged as a powerful tool to take into account topological features stretching over several slices. In the present paper, we illustrate how persistence vineyards can be used to design rigorous statistical hypothesis tests for 3D microstructure models based on data from 2D slices. More precisely, by establishing the asymptotic normality of suitable longitudinal and cross-sectional summary statistics, we devise goodness-of-fit tests that become asymptotically exact in large sampling windows. We illustrate the test…

Computational Geometry (cs.CG)FOS: Computer and information sciencesStatistics and ProbabilityGoodness-of-fit testsApplied MathematicsTopological data analysisPersistence diagramMathematics - Statistics TheoryStatistics Theory (math.ST)VineyardsMaterials scienceComputational MathematicsComputational Theory and Mathematics60F05Topological data analysis Persistence diagram Materials science Vineyards Goodness-of-fit tests Asymptotic normalityFOS: MathematicsAlgebraic Topology (math.AT)Computer Science - Computational GeometryAsymptotic normalityMathematics - Algebraic TopologyComputational Statistics & Data Analysis
researchProduct

Local Asymptotic Normality for Shape and Periodicity in the Drift of a Time Inhomogeneous Diffusion

2017

We consider a one-dimensional diffusion whose drift contains a deterministic periodic signal with unknown periodicity $T$ and carrying some unknown $d$-dimensional shape parameter $\theta$. We prove Local Asymptotic Normality (LAN) jointly in $\theta$ and $T$ for the statistical experiment arising from continuous observation of this diffusion. The local scale turns out to be $n^{-1/2}$ for the shape parameter and $n^{-3/2}$ for the periodicity which generalizes known results about LAN when either $\theta$ or $T$ is assumed to be known.

Statistics and ProbabilityLocal asymptotic normalityMathematical analysisLocal scale62F12 60J60020206 networking & telecommunicationsMathematics - Statistics Theory02 engineering and technologyStatistics Theory (math.ST)01 natural sciencesShape parameterPeriodic function010104 statistics & probability0202 electrical engineering electronic engineering information engineeringFOS: Mathematics0101 mathematicsDiffusion (business)Mathematics
researchProduct

Affine-invariant rank tests for multivariate independence in independent component models

2016

We consider the problem of testing for multivariate independence in independent component (IC) models. Under a symmetry assumption, we develop parametric and nonparametric (signed-rank) tests. Unlike in independent component analysis (ICA), we allow for the singular cases involving more than one Gaussian independent component. The proposed rank tests are based on componentwise signed ranks, à la Puri and Sen. Unlike the Puri and Sen tests, however, our tests (i) are affine-invariant and (ii) are, for adequately chosen scores, locally and asymptotically optimal (in the Le Cam sense) at prespecified densities. Asymptotic local powers and asymptotic relative efficiencies with respect to Wilks’…

Statistics and ProbabilityMultivariate statisticssingular information matricesRank (linear algebra)Gaussianuniform local asymptotic02 engineering and technology01 natural sciencesdistribution-free testsCombinatoricstests for multivariate independence010104 statistics & probabilitysymbols.namesakenormaalius0202 electrical engineering electronic engineering information engineeringApplied mathematics0101 mathematicsStatistique mathématiqueIndependence (probability theory)Parametric statisticsMathematicsDistribution-free testsuniform local asymptotic normalityNonparametric statistics020206 networking & telecommunicationsIndependent component analysisrank testsAsymptotically optimal algorithmsymbolsindependent component models62H1562G35Statistics Probability and UncertaintyUniform local asymptotic normality62G10
researchProduct