Search results for "Almost surely"

showing 10 items of 10 documents

On an approximation problem for stochastic integrals where random time nets do not help

2006

Abstract Given a geometric Brownian motion S = ( S t ) t ∈ [ 0 , T ] and a Borel measurable function g : ( 0 , ∞ ) → R such that g ( S T ) ∈ L 2 , we approximate g ( S T ) - E g ( S T ) by ∑ i = 1 n v i - 1 ( S τ i - S τ i - 1 ) where 0 = τ 0 ⩽ ⋯ ⩽ τ n = T is an increasing sequence of stopping times and the v i - 1 are F τ i - 1 -measurable random variables such that E v i - 1 2 ( S τ i - S τ i - 1 ) 2 ∞ ( ( F t ) t ∈ [ 0 , T ] is the augmentation of the natural filtration of the underlying Brownian motion). In case that g is not almost surely linear, we show that one gets a lower bound for the L 2 -approximation rate of 1 / n if one optimizes over all nets consisting of n + 1 stopping time…

Statistics and ProbabilityRandom time netsMeasurable functionStochastic processStochastic integralsApplied MathematicsUpper and lower boundsNatural filtrationCombinatoricsModeling and SimulationStopping timeModelling and SimulationAlmost surelyApproximationBorel measureBrownian motionMathematicsStochastic Processes and their Applications
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Duopoly signal jamming

1993

This paper examines a repeated duopoly market with heterogeneous outputs. Firms have (common) prior beliefs over the values of an unknown parameter of each firm's demand curve. Firms cannot observe rivals' quantities, but can observe market prices, which are subject to random disturbances and hence provide noisy information that firms use to update their beliefs concerning the unknown parameters' values. Each firm can potentially signal jam, or strategically vary its output level in order to manipulate the distribution of likely market prices and hence the likely inferences drawn by the opponent. We find that the opportunity to signal-jam introduces two conflicting effects, arising out of t…

MicroeconomicsEconomics and EconometricsOrder (exchange)business.industryDemand curveRadio jammingEconomicsMarket priceDistribution (economics)Almost surelybusinessDuopolyPublic financeEconomic Theory
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Modeling the coupled return-spread high frequency dynamics of large tick assets

2015

Large tick assets, i.e. assets where one tick movement is a significant fraction of the price and bid-ask spread is almost always equal to one tick, display a dynamics in which price changes and spread are strongly coupled. We introduce a Markov-switching modeling approach for price change, where the latent Markov process is the transition between spreads. We then use a finite Markov mixture of logit regressions on past squared returns to describe the dependence of the probability of price changes. The model can thus be seen as a Double Chain Markov Model. We show that the model describes the shape of return distribution at different time aggregations, volatility clustering, and the anomalo…

Statistics and ProbabilityComputer Science::Computer Science and Game TheoryVolatility clusteringQuantitative Finance - Trading and Market MicrostructureMarkov chainLogitMarkov processStatistical and Nonlinear PhysicsMarkov modelmodels of financial markets nonlinear dynamics stochastic processesTrading and Market Microstructure (q-fin.TR)FOS: Economics and businesssymbols.namesakesymbolsEconometricsKurtosisFraction (mathematics)Almost surelyStatistics Probability and Uncertainty60J20Mathematics
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A fast and recursive algorithm for clustering large datasets with k-medians

2012

Clustering with fast algorithms large samples of high dimensional data is an important challenge in computational statistics. Borrowing ideas from MacQueen (1967) who introduced a sequential version of the $k$-means algorithm, a new class of recursive stochastic gradient algorithms designed for the $k$-medians loss criterion is proposed. By their recursive nature, these algorithms are very fast and are well adapted to deal with large samples of data that are allowed to arrive sequentially. It is proved that the stochastic gradient algorithm converges almost surely to the set of stationary points of the underlying loss criterion. A particular attention is paid to the averaged versions, which…

Statistics and ProbabilityClustering high-dimensional dataFOS: Computer and information sciencesMathematical optimizationhigh dimensional dataMachine Learning (stat.ML)02 engineering and technologyStochastic approximation01 natural sciencesStatistics - Computation010104 statistics & probabilityk-medoidsStatistics - Machine Learning[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]stochastic approximation0202 electrical engineering electronic engineering information engineeringComputational statisticsrecursive estimatorsAlmost surely[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]0101 mathematicsCluster analysisComputation (stat.CO)Mathematicsaveragingk-medoidsRobbins MonroApplied MathematicsEstimator[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]stochastic gradient[ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]MedoidComputational MathematicsComputational Theory and Mathematicsonline clustering020201 artificial intelligence & image processingpartitioning around medoidsAlgorithm
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Martingale Convergence Theorems and Their Applications

2020

We became familiar with martingales X=(X n ) n∈N0 as fair games and found that under certain transformations (optional stopping, discrete stochastic integral) martingales turn into martingales. In this chapter, we will see that under weak conditions (non-negativity or uniform integrability) martingales converge almost surely. Furthermore, the martingale structure implies L p -convergence under assumptions that are (formally) weaker than those of Chapter 7. The basic ideas of this chapter are Doob’s inequality (Theorem 11.4) and the upcrossing inequality (Lemma 11.3).

Doob's martingale inequalityUniform integrabilityPure mathematicsDoob's martingale convergence theoremsLocal martingaleAlmost surelyMartingale (probability theory)Stock priceStochastic integralMathematics
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Visible parts of fractal percolation

2009

We study dimensional properties of visible parts of fractal percolation in the plane. Provided that the dimension of the fractal percolation is at least 1, we show that, conditioned on non-extinction, almost surely all visible parts from lines are 1-dimensional. Furthermore, almost all of them have positive and finite Hausdorff measure. We also verify analogous results for visible parts from points. These results are motivated by an open problem on the dimensions of visible parts.

28A80Plane (geometry)General MathematicsOpen problemProbability (math.PR)Mathematical analysisFractalDimension (vector space)Mathematics - Classical Analysis and ODEsPercolationHausdorff dimensionClassical Analysis and ODEs (math.CA)FOS: MathematicsHausdorff measureAlmost surelyMathematics - ProbabilityMathematics
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Spatial Search by Quantum Walk is Optimal for Almost all Graphs.

2015

The problem of finding a marked node in a graph can be solved by the spatial search algorithm based on continuous-time quantum walks (CTQW). However, this algorithm is known to run in optimal time only for a handful of graphs. In this work, we prove that for Erd\"os-Renyi random graphs, i.e.\ graphs of $n$ vertices where each edge exists with probability $p$, search by CTQW is \textit{almost surely} optimal as long as $p\geq \log^{3/2}(n)/n$. Consequently, we show that quantum spatial search is in fact optimal for \emph{almost all} graphs, meaning that the fraction of graphs of $n$ vertices for which this optimality holds tends to one in the asymptotic limit. We obtain this result by provin…

Random graphDiscrete mathematicsQuantum PhysicsFOS: Physical sciencesGeneral Physics and AstronomyQuantum entanglement01 natural sciences010305 fluids & plasmasIndifference graphChordal graphQuantum mechanics0103 physical sciencesAlmost surelyQuantum walkQuantum informationQuantum Physics (quant-ph)010306 general physicsQuantum information scienceMathematicsMathematicsofComputing_DISCRETEMATHEMATICSPhysical review letters
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Fractional generalized cumulative entropy and its dynamic version

2021

Following the theory of information measures based on the cumulative distribution function, we propose the fractional generalized cumulative entropy, and its dynamic version. These entropies are particularly suitable to deal with distributions satisfying the proportional reversed hazard model. We study the connection with fractional integrals, and some bounds and comparisons based on stochastic orderings, that allow to show that the proposed measure is actually a variability measure. The investigation also involves various notions of reliability theory, since the considered dynamic measure is a suitable extension of the mean inactivity time. We also introduce the empirical generalized fract…

FOS: Computer and information sciencesExponential distributionComputer Science - Information TheoryMathematics - Statistics TheoryStatistics Theory (math.ST)01 natural sciencesMeasure (mathematics)010305 fluids & plasmas0103 physical sciencesFOS: MathematicsApplied mathematicsAlmost surelyCumulative entropy; Fractional calculus; Stochastic orderings; EstimationEntropy (energy dispersal)010306 general physicsStochastic orderingsMathematicsCentral limit theoremNumerical AnalysisInformation Theory (cs.IT)Applied MathematicsCumulative distribution functionProbability (math.PR)Fractional calculusEmpirical measureFractional calculusModeling and SimulationEstimationCumulative entropyMathematics - ProbabilityCommunications in Nonlinear Science and Numerical Simulation
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Distribution of Large Eigenvalues for Elliptic Operators

2019

In this chapter we consider elliptic differential operators on a compact manifold and rather than taking the semi-classical limit (h →), we let h = 1 and study the distribution of large eigenvalues. Bordeaux Montrieux (Loi de Weyl presque sure et resolvante pour des operateurs differentiels non-autoadjoints, these, CMLS, Ecole Polytechnique, 2008. https://pastel.archives-ouvertes.fr/pastel-00005367, Ann Henri Poincare 12:173–204, 2011) studied elliptic systems of differential operators on S1 with random perturbations of the coefficients, and under some additional assumptions, he showed that the large eigenvalues obey the Weyl law almost surely. His analysis was based on a reduction to the s…

symbols.namesakePure mathematicsElliptic operatorDistribution (mathematics)Weyl lawPoincaré conjecturesymbolsAlmost surelyDifferential operatorEigenvalues and eigenvectorsManifoldMathematics
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Exponential inequalities and estimation of conditional probabilities

2006

This paper deals with the problems of typicality and conditional typicality of “empirical probabilities” for stochastic process and the estimation of potential functions for Gibbs measures and dynamical systems. The questions of typicality have been studied in [FKT88] for independent sequences, in [BRY98, Ris89] for Markov chains. In order to prove the consistency of estimators of transition probability for Markov chains of unknown order, results on typicality and conditional typicality for some (Ψ)-mixing process where obtained in [CsS, Csi02]. Unfortunately, lots of natural mixing process do not satisfy this Ψ -mixing condition (see [DP05]). We consider a class of mixing process inspired …

Discrete mathematicssymbols.namesakeChain rule (probability)Mixing (mathematics)Markov chainStatisticssymbolsLaw of total probabilityConditional probabilityAlmost surelyGibbs measureConditional varianceMathematics
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