Search results for "convergence"

showing 10 items of 655 documents

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
researchProduct

Stochastic order characterization of uniform integrability and tightness

2013

We show that a family of random variables is uniformly integrable if and only if it is stochastically bounded in the increasing convex order by an integrable random variable. This result is complemented by proving analogous statements for the strong stochastic order and for power-integrable dominating random variables. Especially, we show that whenever a family of random variables is stochastically bounded by a p-integrable random variable for some p>1, there is no distinction between the strong order and the increasing convex order. These results also yield new characterizations of relative compactness in Wasserstein and Prohorov metrics.

Statistics and ProbabilityDiscrete mathematicsPure mathematicsRandom fieldMultivariate random variableProbability (math.PR)ta111Random functionRandom element60E15 60B10 60F25Stochastic orderingFunctional Analysis (math.FA)Mathematics - Functional AnalysisRandom variateConvergence of random variablesStochastic simulationFOS: MathematicsStatistics Probability and UncertaintyMathematics - ProbabilityMathematicsStatistics & Probability Letters
researchProduct

Mean square rate of convergence for random walk approximation of forward-backward SDEs

2020

AbstractLet (Y,Z) denote the solution to a forward-backward stochastic differential equation (FBSDE). If one constructs a random walk$B^n$from the underlying Brownian motionBby Skorokhod embedding, one can show$L_2$-convergence of the corresponding solutions$(Y^n,Z^n)$to$(Y, Z).$We estimate the rate of convergence based on smoothness properties, especially for a terminal condition function in$C^{2,\alpha}$. The proof relies on an approximative representation of$Z^n$and uses the concept of discretized Malliavin calculus. Moreover, we use growth and smoothness properties of the partial differential equation associated to the FBSDE, as well as of the finite difference equations associated to t…

Statistics and ProbabilityDiscretizationapproximation schemeMalliavin calculus01 natural sciences010104 statistics & probabilityconvergence rateMathematics::ProbabilityConvergence (routing)random walk approximation 2010 Mathematics Subject Classification: Primary 60H10FOS: MathematicsApplied mathematics0101 mathematicsBrownian motionrandom walk approximationMathematicsstokastiset prosessitSmoothness (probability theory)konvergenssiApplied Mathematics010102 general mathematicsProbability (math.PR)Backward stochastic differential equationsFunction (mathematics)Random walkfinite difference equation[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Rate of convergencebackward stochastic differential equations60G50 Secondary 60H3060H35approksimointidifferentiaaliyhtälötMathematics - Probability
researchProduct

A spatially filtered mixture of β-convergence regressions for EU regions, 1980–2002

2007

Assessing regional growth and convergence across Europe is a matter of primary relevance. Empirical models that do not account for structural heterogeneities and spatial effects may face serious misspecification problems. In this work, a mixture regression approach is applied to the beta-convergence model, in order to produce an endogenous selection of regional growth patterns. A priori choices, such as North-South or centre-periphery divisions, are avoided. In addition to this, we deal with the spatial dependence existing in the data, applying a local filter to the data. The results indicate that spatial effects matter, and either absolute, conditional, or club convergence, if extended to …

Statistics and ProbabilityEconomics and EconometricsSmall numberEmpirical modellingSample (statistics)Filter (signal processing)Mathematics (miscellaneous)Rate of convergenceConvergence (routing)StatisticsOutlierEconometricsSpatial dependenceSettore SECS-P/01 - Economia PoliticaRegional growth - Convergence patterns - Mixture regression - Spatial effectsSocial Sciences (miscellaneous)MathematicsEmpirical Economics
researchProduct

Large deviations results for subexponential tails, with applications to insurance risk

1996

AbstractConsider a random walk or Lévy process {St} and let τ(u) = inf {t⩾0 : St > u}, P(u)(·) = P(· | τ(u) < ∞). Assuming that the upwards jumps are heavy-tailed, say subexponential (e.g. Pareto, Weibull or lognormal), the asymptotic form of the P(u)-distribution of the process {St} up to time τ(u) is described as u → ∞. Essentially, the results confirm the folklore that level crossing occurs as result of one big jump. Particular sharp conclusions are obtained for downwards skip-free processes like the classical compound Poisson insurance risk process where the formulation is in terms of total variation convergence. The ideas of the proof involve excursions and path decompositions for Mark…

Statistics and ProbabilityExponential distributionRegular variationRuin probabilityExcursionRandom walkDownwards skip-free processLévy processConditioned limit theoremTotal variation convergenceCombinatoricsInsurance riskPath decompositionIntegrated tailProbability theoryModelling and SimulationExtreme value theoryMaximum domain of attractionMathematicsStochastic processApplied MathematicsExtreme value theoryRandom walkSubexponential distributionModeling and SimulationLog-normal distributionLarge deviations theory60K1060F10Stochastic Processes and their Applications
researchProduct

Price convergence of peripheral European countries on the way to the EMU: A time series approach

2000

This paper examines price and inflation convergence between three European countries (Italy, Spain and the U.K.) and a European average and, alternatively, between them and Germany from the beginning of the 80's.

Statistics and ProbabilityInflationMacroeconomicsEconomics and EconometricsCointegrationmedia_common.quotation_subjectKeynesian economicsTime series approachConvergence (economics)Nominal convergence unit root cointegration time-varying parametersjel:C22jel:E31Mathematics (miscellaneous)jel:F15EconomicsUnit rootSocial Sciences (miscellaneous)media_common
researchProduct

Wardowski conditions to the coincidence problem

2015

In this article we first discuss the existence and uniqueness of a solution for the coincidence problem: Find p ∈ X such that Tp = Sp, where X is a nonempty set, Y is a complete metric space, and T, S:X → Y are two mappings satisfying a Wardowski type condition of contractivity. Later on, we will state the convergence of the Picard-Juncgk iteration process to the above coincidence problem as well as a rate of convergence for this iteration scheme. Finally, we shall apply our results to study the existence and uniqueness of a solution as well as the convergence of the Picard-Juncgk iteration process toward the solution of a second order differential equation. Ministerio de Economía y Competi…

Statistics and ProbabilityIterative methodsIterative methodCoincidence pointsComplete metric space54H25common fixed pointsConvergence (routing)Applied mathematicsUniquenessMathematicsApplied Mathematics and Statistics47J25lcsh:T57-57.97Applied MathematicsMathematical analysisOrder (ring theory)State (functional analysis)Rate of convergencecoincidence pointsRate of convergenceordinary differential equationsOrdinary differential equationlcsh:Applied mathematics. Quantitative methodsCommon fixed pointsiterative methodslcsh:Probabilities. Mathematical statisticslcsh:QA273-280rate of convergenceFrontiers in Applied Mathematics and Statistics
researchProduct

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
researchProduct

Distribution of oxygen partial pressure in a two-dimensional tissue supplied by capillary meshes and concurrent and countercurrent systems

1969

Abstract For the calculations of oxygen partial pressure in a two-dimensional tissue model supplied by a capillary network (inhomogeneously perfused tissue), two differential equations are given that describe the process in the tissue and capillaries. The differential equations are coupled by the boundary conditions. Results obtained by using the method of successive displacements are given for the two-dimensional problem. This method exhibits a satisfactory convergence. The accuracy of the results is about ±5% based on the initial concentration. The results for the network model are compared with those for equivalent concurrent and countercurrent systems. Equivalence means in this connecti…

Statistics and ProbabilityMaterials scienceGeneral Immunology and MicrobiologyDifferential equationCapillary actionCountercurrent exchangeQuantitative Biology::Tissues and OrgansApplied MathematicsPhysics::Medical PhysicsGeneral MedicinePartial pressureMechanicsAnatomyGeneral Biochemistry Genetics and Molecular BiologyDistribution (mathematics)Modeling and SimulationConvergence (routing)Boundary value problemGeneral Agricultural and Biological SciencesNetwork modelMathematical Biosciences
researchProduct

Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter

2013

Markov chain Monte Carlo (MCMC) methods are powerful computational tools for analysis of complex statistical problems. However, their computational efficiency is highly dependent on the chosen proposal distribution, which is generally difficult to find. One way to solve this problem is to use adaptive MCMC algorithms which automatically tune the statistics of a proposal distribution during the MCMC run. A new adaptive MCMC algorithm, called the variational Bayesian adaptive Metropolis (VBAM) algorithm, is developed. The VBAM algorithm updates the proposal covariance matrix using the variational Bayesian adaptive Kalman filter (VB-AKF). A strong law of large numbers for the VBAM algorithm is…

Statistics and ProbabilityMathematical optimizationCovariance matrixApplied MathematicsBayesian probabilityRejection samplingMathematics - Statistics TheoryMarkov chain Monte CarloStatistics Theory (math.ST)Kalman filterStatistics::ComputationComputational Mathematicssymbols.namesakeComputingMethodologies_PATTERNRECOGNITIONMetropolis–Hastings algorithmComputational Theory and MathematicsConvergence (routing)FOS: MathematicsKernel adaptive filtersymbolsMathematicsComputational Statistics &amp; Data Analysis
researchProduct