Search results for "Tributi"

showing 10 items of 6415 documents

Truncation, Information, and the Coefficient of Variation

1989

The Fisher information in a random sample from the truncated version of a distribution that belongs to an exponential family is compared with the Fisher information in a random sample from the un- truncated distribution. Conditions under which there is more information in the selection sample are given. Examples involving the normal and gamma distributions with various selection sets, and the zero-truncated binomial, Poisson, and negative binomial distributions are discussed. A property pertaining to the coefficient of variation of certain discrete distributions on the non-negative integers is introduced and shown to be satisfied by all binomial, Poisson, and negative binomial distributions.

symbols.namesakeExponential familyBinomial (polynomial)Negative binomial distributionsymbolsGamma distributionApplied mathematicsProbability distributionTruncation (statistics)Poisson distributionMathematicsTruncated distribution
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On the autocorrelation function of Rice processes for unsymmetrical doppler power spectral densities

2010

In this paper, we derive an analytical expression for the ACF of Rice processes in the general case of unsymmetrical Doppler power spectral densities. This expression, which is obtained based on the multidimensional Gaussian distribution approach, is shown to cover the ACF of Rayleigh processes as a special case. Various numerical examples are presented to illustrate the impact of the channel parameters on the ACF. Computer simulations, considering the von Mises distribution for the angle of arrivals, are also performed to check the validity of the analytical result. Finally, the analysis of the covariance spectrum is addressed.

symbols.namesakeGaussianAutocorrelationStatisticssymbolsvon Mises distributionStatistical physicsRayleigh scatteringCovarianceDoppler effectMathematicsPower (physics)Rayleigh fadingThe 2010 International Conference on Advanced Technologies for Communications
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Non-Stationary Probabilistic Response of Linear Systems Under Non-Gaussian Input

1991

The probabilistic characterization of the response of linear systems subjected to non-normal input requires the evaluation of higher order moments than two. In order to obtain the equations governing these moments, in this paper the extension of the Ito’s differential rule for linear systems excited by non-normal delta correlated processes is presented. As an application the case of the delta correlated compound Poisson input process is treated.

symbols.namesakeGaussianLinear systemsymbolsProbabilistic logicProcess (computing)Order (ring theory)Applied mathematicsExtension (predicate logic)Differential (infinitesimal)Poisson distributionMathematics
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Representation of Stationary Multivariate Gaussian Processes Fractional Differential Approach

2011

In this paper, the fractional spectral moments method (H-FSM) is used to generate stationary Gaussian multivariate processes with assigned power spectral density matrix. To this aim, firstly the N-variate process is expressed as sum of N fully coherent normal random vectors, and then, the representation in terms of HFSM is used.

symbols.namesakeMathematical analysissymbolsRepresentation (systemics)Applied mathematicsMultivariate normal distributionMultivariate Processes Fractional Calculus Fractional Spectral MomentsFractional differentialSettore ICAR/08 - Scienza Delle CostruzioniGaussian processMathematicsProceedings of the 6th International Conference on Computational Stochastic Mechanics(CSM-6)
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Modelling Systemic Cojumps with Hawkes Factor Models

2013

Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating a set of 20 high cap stocks traded at the Italian Stock Exchange, we find that there is a large number of high frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets.

symbols.namesakeMultivariate statisticsStock exchangeEconometricssymbolsEconomicsPoisson distributionSynchronizationTime clusteringFactor analysisSign (mathematics)SSRN Electronic Journal
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Non Linear Systems Under Complex α-Stable Le´vy White Noise

2003

The problem of predicting the response of linear and nonlinear systems under Levy white noises is examined. A method of analysis is proposed based on the observation that these processes have impulsive character, so that the methods already used for Poisson white noise or normal white noise may be also recast for Levy white noises. Since both the input and output processes have no moments of order two and higher, the response is here evaluated in terms of characteristic function.Copyright © 2003 by ASME

symbols.namesakeNonlinear systemAdditive white Gaussian noiseControl theoryStochastic resonanceGaussian noiseMathematical analysissymbolsBrownian noiseImpulsive characterWhite noisePsychologyPoisson distributionApplied Mechanics and Biomedical Technology
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Convergence of Measures

2020

One focus of probability theory is distributions that are the result of an interplay of a large number of random impacts. Often a useful approximation can be obtained by taking a limit of such distributions, for example, a limit where the number of impacts goes to infinity. With the Poisson distribution, we have encountered such a limit distribution that occurs as the number of very rare events when the number of possibilities goes to infinity (see Theorem 3.7). In many cases, it is necessary to rescale the original distributions in order to capture the behavior of the essential fluctuations, e.g., in the central limit theorem. While these theorems work with real random variables, we will a…

symbols.namesakeProbability theoryWeak convergencesymbolsLimit (mathematics)Statistical physicsPoisson distributionConvergence of measuresRandom variableBrownian motionMathematicsCentral limit theorem
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Riesz-Fischer Maps, Semi-frames and Frames in Rigged Hilbert Spaces

2021

In this note we present a review, some considerations and new results about maps with values in a distribution space and domain in a σ-finite measure space X. Namely, this is a survey about Bessel maps, frames and bases (in particular Riesz and Gel’fand bases) in a distribution space. In this setting, the Riesz-Fischer maps and semi-frames are defined and new results about them are obtained. Some examples in tempered distributions space are examined.

symbols.namesakePure mathematicsDistribution (mathematics)Settore MAT/05 - Analisi MatematicasymbolsHilbert spaceRigged Hilbert spaceSpace (mathematics)Measure (mathematics)Frames Bases Distributions Rigged Hilbert spaceBessel functionDomain (mathematical analysis)Mathematics
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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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Regularity of a Degenerated Convolution Semi-Group Without to Use the Poisson Process

2011

We translate in semi-group theory our regularity result for a degenerated convolution semi-group got by the Malliavin Calculus of Bismut type for Poisson processes.

symbols.namesakePure mathematicsMathematics::ProbabilityGroup (mathematics)symbolsPoisson processType (model theory)Poisson distributionMalliavin calculusMathematicsConvolution
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