Search results for "62"

showing 10 items of 970 documents

Recursive estimation of the conditional geometric median in Hilbert spaces

2012

International audience; A recursive estimator of the conditional geometric median in Hilbert spaces is studied. It is based on a stochastic gradient algorithm whose aim is to minimize a weighted L1 criterion and is consequently well adapted for robust online estimation. The weights are controlled by a kernel function and an associated bandwidth. Almost sure convergence and L2 rates of convergence are proved under general conditions on the conditional distribution as well as the sequence of descent steps of the algorithm and the sequence of bandwidths. Asymptotic normality is also proved for the averaged version of the algorithm with an optimal rate of convergence. A simulation study confirm…

Statistics and ProbabilityMallows-Wasserstein distanceRobbins-Monroasymptotic normalityCLTcentral limit theoremAsymptotic distributionMathematics - Statistics TheoryStatistics Theory (math.ST)01 natural sciencesMallows–Wasserstein distanceonline data010104 statistics & probability[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]60F05FOS: MathematicsApplied mathematics[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]0101 mathematics62L20MathematicsaveragingSequential estimation010102 general mathematicsEstimatorRobbins–MonroConditional probability distribution[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]Geometric medianstochastic gradient[ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]robust estimatorRate of convergenceConvergence of random variablesStochastic gradient.kernel regressionsequential estimationKernel regressionStatistics Probability and Uncertainty
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Criteria for Bayesian model choice with application to variable selection

2012

In objective Bayesian model selection, no single criterion has emerged as dominant in defining objective prior distributions. Indeed, many criteria have been separately proposed and utilized to propose differing prior choices. We first formalize the most general and compelling of the various criteria that have been suggested, together with a new criterion. We then illustrate the potential of these criteria in determining objective model selection priors by considering their application to the problem of variable selection in normal linear models. This results in a new model selection objective prior with a number of compelling properties.

Statistics and ProbabilityMathematical optimization62C10Model selectiong-priorLinear modelMathematics - Statistics TheoryFeature selectionStatistics Theory (math.ST)Model selectionBayesian inferenceObjective model62J05Prior probability62J15FOS: MathematicsStatistics Probability and Uncertaintyobjective BayesSelection (genetic algorithm)variable selectionMathematicsThe Annals of Statistics
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Uniform convergence and asymptotic confidence bands for model-assisted estimators of the mean of sampled functional data

2013

When the study variable is functional and storage capacities are limited or transmission costs are high, selecting with survey sampling techniques a small fraction of the observations is an interesting alternative to signal compression techniques, particularly when the goal is the estimation of simple quantities such as means or totals. We extend, in this functional framework, model-assisted estimators with linear regression models that can take account of auxiliary variables whose totals over the population are known. We first show, under weak hypotheses on the sampling design and the regularity of the trajectories, that the estimator of the mean function as well as its variance estimator …

Statistics and ProbabilityMean squared errorMathematics - Statistics TheoryStatistics Theory (math.ST)Hájek estimator62D05; 62E20 62M9901 natural sciences010104 statistics & probabilityMinimum-variance unbiased estimatorBias of an estimator[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]60F050502 economics and businessStatisticsConsistent estimatorFOS: Mathematicscovariance functionHorvitz-Thompson estimator[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]62L200101 mathematicssurvey sampling050205 econometrics Variance functionMathematicsGREG05 social sciencesEstimator[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]calibration[ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]linear interpolation.linear interpolationEfficient estimatorStatistics Probability and Uncertaintyfunctional linear modelInvariant estimator
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Fractional calculus approach to the statistical characterization of random variables and vectors

2009

Fractional moments have been investigated by many authors to represent the density of univariate and bivariate random variables in different contexts. Fractional moments are indeed important when the density of the random variable has inverse power-law tails and, consequently, it lacks integer order moments. In this paper, starting from the Mellin transform of the characteristic function and by fractional calculus method we present a new perspective on the statistics of random variables. Introducing the class of complex moments, that include both integer and fractional moments, we show that every random variable can be represented within this approach, even if its integer moments diverge. A…

Statistics and ProbabilityMellin transformStatistical Mechanics (cond-mat.stat-mech)Characteristic function (probability theory)Multivariate distributionMultivariate random variableMathematical analysisFOS: Physical sciencesMoment-generating functionCondensed Matter PhysicsFractional calculusFractional and complex moments; Multivariate distributions; Power-law tails; Inverse Mellin transformFractional and complex momentIngenieurwissenschaftenApplied mathematicsddc:620Inverse Mellin transformSettore ICAR/08 - Scienza Delle CostruzioniRandom variableCondensed Matter - Statistical MechanicsMathematicsInteger (computer science)Taylor expansions for the moments of functions of random variablesPower-law tail
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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
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Regression models for multivariate ordered responses via the Plackett distribution

2008

AbstractWe investigate the properties of a class of discrete multivariate distributions whose univariate marginals have ordered categories, all the bivariate marginals, like in the Plackett distribution, have log-odds ratios which do not depend on cut points and all higher-order interactions are constrained to 0. We show that this class of distributions may be interpreted as a discretized version of a multivariate continuous distribution having univariate logistic marginals. Convenient features of this class relative to the class of ordered probit models (the discretized version of the multivariate normal) are highlighted. Relevant properties of this distribution like quadratic log-linear e…

Statistics and ProbabilityNumerical AnalysisMultivariate statisticsGlobal logitsLogistic distributionUnivariateMultivariate normal distributionmultivariate ordered responseProportional oddsBivariate analysisMarginal modelsPlackett distribution.Plackett distributionUnivariate distribution62H05Statistics62J12Statistics::Methodology60E15Statistics Probability and UncertaintyMarginal distributionMultivariate ordered regressionMathematicsMultivariate stable distributionJournal of Multivariate Analysis
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Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data

2015

The paper describes the use of frequentist and Bayesian shared-parameter joint models of longitudinal measurements of prostate-specific antigen (PSA) and the risk of prostate cancer (PCa). The motivating dataset corresponds to the screening arm of the Spanish branch of the European Randomized Screening for Prostate Cancer study. The results show that PSA is highly associated with the risk of being diagnosed with PCa and that there is an age-varying effect of PSA on PCa risk. Both the frequentist and Bayesian paradigms produced very close parameter estimates and subsequent 95% confidence and credibility intervals. Dynamic estimations of disease-free probabilities obtained using Bayesian infe…

Statistics and ProbabilityPREDICTIONBayesian probabilityurologic and male genital diseasesBayesian inferenceGeneralized linear mixed modelPSAProstate cancerLATENT CLASS MODELSAnàlisi de supervivència (Biometria)Frequentist inference62N01Statisticsprostate cancer screeningSurvival analysis (Biometry)FAILUREMedicineProstate cancer riskTO-EVENT DATAbusiness.industryjoint modelsMORTALITYDISEASE PROGRESSIONmedicine.diseaselinear mixed modelsTIMEProstate-specific antigenProstate cancer screeningshared-parameter models:Matemàtiques i estadística::Estadística matemàtica [Àrees temàtiques de la UPC]62P10SURVIVALStatistics Probability and Uncertaintyrelative risk modelsFOLLOW-UPbusinessJournal of Applied Statistics
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Varying-coefficient functional linear regression models

2008

This article considers a generalization of the functional linear regression in which an additional real variable influences smoothly the functional coefficient. We thus define a varying-coefficient regression model for functional data. We propose two estimators based, respectively, on conditional functional principal regression and on local penalized regression splines and prove their pointwise consistency. We check, with the prediction one day ahead of ozone concentration in the city of Toulouse, the ability of such nonlinear functional approaches to produce competitive estimations.

Statistics and ProbabilityPolynomial regressionStatistics::TheoryProper linear modelMultivariate adaptive regression splines010504 meteorology & atmospheric sciencesLocal regression01 natural sciences62G05 (62G20 62M20)Statistics::ComputationNonparametric regressionStatistics::Machine Learning010104 statistics & probabilityLinear regressionStatisticsStatistics::Methodology0101 mathematicsSegmented regressionRegression diagnosticComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciencesMathematics
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Consistent device simulation model describing perovskite solar cells in steady-state, transient, and frequency domain

2019

​This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Applied Materials & Interfaces, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://pubs.acs.org/doi/10.1021/acsami.9b04991

Steady state (electronics)Materials scienceIMPSImpedance spectroscopy610 Medicine & health02 engineering and technology010402 general chemistrycomputer.software_genre01 natural sciencesChemical societyGeneral Materials ScienceTransient (computer programming)Device simulation10266 Clinic for Reconstructive SurgeryMaterials621.3: Elektrotechnik und ElektronikCèl·lules fotoelèctriquesTrapsPerovskite (structure)Drift-diffusion modelingProgramming languagePerovskite solar cellsHysteresis021001 nanoscience & nanotechnology2500 General Materials Science0104 chemical sciencesMobile ionsFrequency domainTransient photo-current0210 nano-technologycomputer
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Antiproliferative agents that interfere with the cell cycle at the G(1)-->S transition: further development and characterization of a small library o…

2008

In this continuation of our research on derivatives containing the stilbene privileged structure or that are derived from it, we report the results of further studies carried out on the previously initiated collection of compounds. We used a parallel synthetic approach to rapidly obtain small sets of compounds and started the annotation of the library in progress by calculating some physicochemical properties to be eventually correlated with biological activities. A pharmacophore for the antiproliferative activity was also built to summarize the features of the library. We evaluated the antiproliferative and pro-apoptotic activities of all compounds as well as the cell-cycle effects of some…

StereochemistryCellular differentiationAntineoplastic AgentsApoptosisHL-60 CellsBiochemistryS PhaseSmall Molecule Librarieschemistry.chemical_compoundInhibitory Concentration 50Biological profileCell Line TumorDrug DiscoveryStilbenespharmacophoresHumansGeneral Pharmacology Toxicology and PharmaceuticsPhosphorylationPharmacologyChemistryOrganic ChemistryG1 PhaseRetinoblastomaSmall Molecule LibrariesG1/S transitionCell DifferentiationCell cycleFlow CytometryCombinatorial chemistryantitumor agentAntiproliferative AgentsMolecular MedicineTriolcell cyclePharmacophoreC-C couplingK562 Cells
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