0000000000347522

AUTHOR

Pierre-andré Zitt

0000-0003-2834-3147

showing 2 related works from this author

Quantitative ergodicity for some switched dynamical systems

2012

International audience; We provide quantitative bounds for the long time behavior of a class of Piecewise Deterministic Markov Processes with state space Rd × E where E is a finite set. The continuous component evolves according to a smooth vector field that switches at the jump times of the discrete coordinate. The jump rates may depend on the whole position of the process. Under regularity assumptions on the jump rates and stability conditions for the vector fields we provide explicit exponential upper bounds for the convergence to equilibrium in terms of Wasserstein distances. As an example, we obtain convergence results for a stochastic version of the Morris-Lecar model of neurobiology.

Statistics and ProbabilitySwitched dynamical systemsDynamical systems theoryMarkov process01 natural sciences34D2393E15010104 statistics & probabilitysymbols.namesakeCouplingPiecewise Deterministic Markov ProcessPosition (vector)60J25FOS: MathematicsState spaceApplied mathematicsWasserstein distance0101 mathematicsMathematicsProbability (math.PR)010102 general mathematicsErgodicityErgodicity[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Linear Differential EquationsPiecewisesymbolsJumpAMS-MSC. 60J75; 60J25; 93E15; 34D23Vector fieldStatistics Probability and Uncertainty60J75[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - Probability
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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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