Search results for "Random"

showing 10 items of 3931 documents

Rare events and scaling properties in field-induced anomalous dynamics

2012

We show that, in a broad class of continuous time random walks (CTRW), a small external field can turn diffusion from standard into anomalous. We illustrate our findings in a CTRW with trapping, a prototype of subdiffusion in disordered and glassy materials, and in the L\'evy walk process, which describes superdiffusion within inhomogeneous media. For both models, in the presence of an external field, rare events induce a singular behavior in the originally Gaussian displacements distribution, giving rise to power-law tails. Remarkably, in the subdiffusive CTRW, the combined effect of highly fluctuating waiting times and of a drift yields a non-Gaussian distribution characterized by long sp…

Statistics and ProbabilityField (physics)GaussianFOS: Physical sciencesQuantitative Biology::Cell Behaviorsymbols.namesaketransport processes/heat transfer (theory). diffusionRare eventsstochastic particle dynamics (theory)Statistical physicsDiffusion (business)ScalingPhysicsdiffusiondriven diffusive systems (theory)Statistical and Nonlinear PhysicsDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksRandom walkDistribution (mathematics)Lévy flighttransport processes/heat transfer (theory)symbolsdiffusion; stochastic particle dynamics (theory); driven diffusive systems (theory); transport processes/heat transfer (theory)Statistics Probability and UncertaintyStatistical and Nonlinear PhysicJournal of Statistical Mechanics: Theory and Experiment
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Subject-specific odds ratios in binomial GLMMs with continuous response

2007

In a regression context, the dichotomization of a continuous outcome variable is often motivated by the need to express results in terms of the odds ratio, as a measure of association between the response and one or more risk factors. Starting from the recent work of Moser and Coombs (Odds ratios for a continuous outcome variable without dichotomizing, Statistics in Medicine, 2004, 23, 1843-1860), in this article we explore in a mixed model framework the possibility of obtaining odds ratio estimates from a regression linear model without the need of dichotomizing the response variable. It is shown that the odds ratio estimators derived from a linear mixed model outperform those from a binom…

Statistics and ProbabilityGeneral linear modelProper linear modelDichotomizingBinomial regressionLinear modelLogistic regressionOdds ratioEfficiencyRandom effects modelLogistic regressionGeneralized linear mixed modelRandom effectStatisticsEconometricsDiagnostic odds ratioStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaMathematics
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On the convenience of heteroscedasticity in highly multivariate disease mapping

2019

Highly multivariate disease mapping has recently been proposed as an enhancement of traditional multivariate studies, making it possible to perform the joint analysis of a large number of diseases. This line of research has an important potential since it integrates the information of many diseases into a single model yielding richer and more accurate risk maps. In this paper we show how some of the proposals already put forward in this area display some particular problems when applied to small regions of study. Specifically, the homoscedasticity of these proposals may produce evident misfits and distorted risk maps. In this paper we propose two new models to deal with the variance-adaptiv…

Statistics and ProbabilityHeteroscedasticityMultivariate statisticsComputer scienceDiseaseJoint analysisMachine learningcomputer.software_genreBayesian statistics01 natural sciencesGaussian Markov random fields010104 statistics & probability03 medical and health sciences0302 clinical medicineHomoscedasticity0101 mathematicsMultivariate disease mappingSpatial analysisMortality studiesInterpretation (logic)Spatial statisticsbusiness.industryBayesian statisticsEstadística bayesianaMalalties030211 gastroenterology & hepatologyArtificial intelligenceStatistics Probability and Uncertaintybusinesscomputer
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The smallest singular value of a shifted $d$-regular random square matrix

2017

We derive a lower bound on the smallest singular value of a random d-regular matrix, that is, the adjacency matrix of a random d-regular directed graph. Specifically, let $$C_1<d< c n/\log ^2 n$$ and let $$\mathcal {M}_{n,d}$$ be the set of all $$n\times n$$ square matrices with 0 / 1 entries, such that each row and each column of every matrix in $$\mathcal {M}_{n,d}$$ has exactly d ones. Let M be a random matrix uniformly distributed on $$\mathcal {M}_{n,d}$$ . Then the smallest singular value $$s_{n} (M)$$ of M is greater than $$n^{-6}$$ with probability at least $$1-C_2\log ^2 d/\sqrt{d}$$ , where c, $$C_1$$ , and $$C_2$$ are absolute positive constants independent of any other parameter…

Statistics and ProbabilityIdentity matrixAdjacency matrices01 natural sciencesSquare matrixCombinatorics010104 statistics & probabilityMatrix (mathematics)Mathematics::Algebraic GeometryFOS: MathematicsMathematics - Combinatorics60B20 15B52 46B06 05C80Adjacency matrix0101 mathematicsCondition numberCondition numberMathematicsRandom graphsRandom graphLittlewood–Offord theorySingularity010102 general mathematicsProbability (math.PR)InvertibilityRegular graphsSingular valueSmallest singular valueAnti-concentrationSingular probabilitySparse matricesCombinatorics (math.CO)Statistics Probability and UncertaintyRandom matricesRandom matrixMathematics - ProbabilityAnalysis
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Uniform measure density condition and game regularity for tug-of-war games

2018

We show that a uniform measure density condition implies game regularity for all 2 < p < ∞ in a stochastic game called “tug-of-war with noise”. The proof utilizes suitable choices of strategies combined with estimates for the associated stopping times and density estimates for the sum of independent and identically distributed random vectors. peerReviewed

Statistics and ProbabilityIndependent and identically distributed random variablesComputer Science::Computer Science and Game Theorygame regularitydensity estimate for the sum of i.i.d. random vectorsTug of war01 natural sciencesMeasure (mathematics)$p$-regularityMathematics - Analysis of PDEsFOS: MathematicsApplied mathematicspeliteoriastochastic games0101 mathematics91A15 60G50 35J92Mathematicsp-harmonic functionsstokastiset prosessit$p$-harmonic functionsosittaisdifferentiaaliyhtälöthitting probability010102 general mathematicsStochastic gametug-of-war gamesProbability (math.PR)uniform measure density condition010101 applied mathematicsNoiseuniform distribution in a ballMathematics - ProbabilityAnalysis of PDEs (math.AP)
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Assessing covariate imbalance in meta-analysis studies.

2010

The main goal of meta-analysis is to combine data across studies or data sets to obtain summary estimates. In this paper, the novelty is to propose a statistical tool to assess a possible covariate imbalance in baseline variables to investigate similarity of trials. We conducted the detection of the covariate imbalance, first, through some graphical comparison of the empirical cumulative distribution functions or ECDFs, which are built by putting together arms or trials according to some risk factor, and second, through some non-parametric tests such as the Kolmogorov–Smirnov and the Anderson–Darling tests. To overcome the huge presence of ties, we conducted the statistical tests on perturbe…

Statistics and ProbabilityMaleperturbationEpidemiologyComputer sciencePoolingHypercholesterolemiaAlpha interferonMeta-Analysis as TopicCovariateStatisticsEconometricsHumansSettore SECS-S/05 - Statistica SocialeECDFnon-parametric testStatistical hypothesis testingRandomized Controlled Trials as TopicCumulative distribution functionNonparametric statisticsNoveltyInterferon-alphacombinabilityHepatitis C ChronicMeta-analysisData Interpretation StatisticalFemaleHydroxymethylglutaryl-CoA Reductase InhibitorsStatistics in medicine
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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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Pairwise Markov properties for regression graphs

2016

With a sequence of regressions, one may generate joint probability distributions. One starts with a joint, marginal distribution of context variables having possibly a concentration graph structure and continues with an ordered sequence of conditional distributions, named regressions in joint responses. The involved random variables may be discrete, continuous or of both types. Such a generating process specifies for each response a conditioning set that contains just its regressor variables, and it leads to at least one valid ordering of all nodes in the corresponding regression graph that has three types of edge: one for undirected dependences among context variables, another for undirect…

Statistics and ProbabilityMarkov chain010102 general mathematicsMixed graphConditional probability distribution01 natural sciencesCombinatorics010104 statistics & probabilityConditional independenceJoint probability distributionMarkov property0101 mathematicsStatistics Probability and UncertaintyMarginal distributionRandom variableMathematicsStat
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Elasticity function of a discrete random variable and its properties

2017

ABSTRACTElasticity (or elasticity function) is a new concept that allows us to characterize the probability distribution of any random variable in the same way as characteristic functions and hazard and reverse hazard functions do. Initially defined for continuous variables, it was necessary to extend the definition of elasticity and study its properties in the case of discrete variables. A first attempt to define discrete elasticity is seen in Veres-Ferrer and Pavia (2014a). This paper develops this definition and makes a comparative study of its properties, relating them to the properties shown by discrete hazard and reverse hazard, as both defined in Chechile (2011). Similar to continuou…

Statistics and ProbabilityMathematical optimization021103 operations researchDiscretizationHazard ratio0211 other engineering and technologies02 engineering and technology01 natural sciencesElasticity of a functionContinuous variable010104 statistics & probabilityApplied mathematicsProbability distribution0101 mathematicsElasticity (economics)Random variableMathematicsCommunications in Statistics - Theory and Methods
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Optimal Reporting of Predictions

1989

Abstract Consider a problem in which you and a group of other experts must report your individual predictive distributions for an observable random variable X to some decision maker. Suppose that the report of each expert is assigned a prior weight by the decision maker and that these weights are then updated based on the observed value of X. In this situation you will try to maximize your updated, or posterior, weight by appropriately choosing the distribution that you report, rather than necessarily simply reporting your honest predictive distribution. We study optimal reporting strategies under various conditions regarding your knowledge and beliefs about X and the reports of the other e…

Statistics and ProbabilityMathematical optimizationExpert opinionStatisticsGaining weightStatistics Probability and UncertaintyDecision makerBayesian inferenceFinite setRandom variableValue (mathematics)WeightingMathematicsJournal of the American Statistical Association
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