Martingale convergence to the Poisson distribution
Maxima of the cells of an equiprobable multinomial.
Maximal arithmetic progressions in random subsets.
Maximal inequalities and some convergence theorems for fuzzy random variables
Some maximal inequalities for quadratic forms of independent and linearly negative quadrant dependent fuzzy random variables are established. Strong convergence of such quadratic forms are proved based on the martingale theory. A weak law of large numbers for linearly negative quadrant dependent fuzzy random variables is stated and proved.
Metastable behaviour of small noise Lévy-Driven diffusions
We consider a dynamical system in driven by a vector field -U', where U is a multi-well potential satisfying some regularity conditions. We perturb this dynamical system by a Lévy noise of small intensity and such that the heaviest tail of its Lévy measure is regularly varying. We show that the perturbed dynamical system exhibits metastable behaviour i.e. on a proper time scale it reminds of a Markov jump process taking values in the local minima of the potential U. Due to the heavy-tail nature...
Metric entropy and the central limit theorem in
Central limit theorems with hypotheses in terms of -entropy are proved first in where is a compact metric space and then in an arbitrary separable Banach space.
Metric properties of alternating Lüroth series
Metric Theorems Related to the Kronecker - Weyl Theorem mod m.
Mild law of large numbers and its consequences
Minimum variance importance sampling via Population Monte Carlo
Variance reduction has always been a central issue in Monte Carlo experiments. Population Monte Carlo can be used to this effect, in that a mixture of importance functions, called a D-kernel, can be iteratively optimized to achieve the minimum asymptotic variance for a function of interest among all possible mixtures. The implementation of this iterative scheme is illustrated for the computation of the price of a European option in the Cox-Ingersoll-Ross model. A Central Limit theorem as well...
Modèles statistiques et systèmes standards de processus de vraisemblance
Moderate deviations for functional U-processes
Moderate deviations for some point measures in geometric probability
Functionals in geometric probability are often expressed as sums of bounded functions exhibiting exponential stabilization. Methods based on cumulant techniques and exponential modifications of measures show that such functionals satisfy moderate deviation principles. This leads to moderate deviation principles and laws of the iterated logarithm for random packing models as well as for statistics associated with germ-grain models and k nearest neighbor graphs.
Moderate deviations for traces of words in a mult-matrix model.
Moment convergence and the law of iterated logarithm for additive functions
Moment measures of heavy-tailed renewal point processes: asymptotics and applications
We study higher-order moment measures of heavy-tailed renewal models, including a renewal point process with heavy-tailed inter-renewal distribution and its continuous analog, the occupation measure of a heavy-tailed Lévy subordinator. Our results reveal that the asymptotic structure of such moment measures are given by explicit power-law density functions. The same power-law densities appear naturally as cumulant measures of certain Poisson and Gaussian stochastic integrals. This correspondence...
Moments of probability measures on a group.
Multidimensional limit theorems for smoothed extreme value estimates of point processes boundaries
In this paper, we give sufficient conditions to establish central limit theorems and moderate deviation principle for a class of support estimates of empirical and Poisson point processes. The considered estimates are obtained by smoothing some bias corrected extreme values of the point process. We show how the smoothing permits to obtain Gaussian asymptotic limits and therefore pointwise confidence intervals. Some unidimensional and multidimensional examples are provided.
Multivariate normal approximation using Stein’s method and Malliavin calculus
We combine Stein’s method with Malliavin calculus in order to obtain explicit bounds in the multidimensional normal approximation (in the Wasserstein distance) of functionals of gaussian fields. Among several examples, we provide an application to a functional version of the Breuer–Major CLT for fields subordinated to a fractional brownian motion.
Multivariate tail equivalence of distributions in extreme value theory