Discrete uniform distribution for a sum of additive functions

We consider the limit distribution of values of a sum of sets of strongly additive arithmetic functions with shifted argument. We obtain sufficient and necessary conditions for a weak convergence of distributions of that sum to the discrete uniform law. The case where those functions take values 0 or 1 on primes is studied.     

Equivalence classes of regularly varying functions

A useful method to derive limit results for partial maxima and record values of independent, identically distributed random variables is to start from one specific probability distribution and to extend the result for this distribution to a class of distributions.This method involves an extended theory of regularly varying functions. In this paper, equivalence classes of regularly varying functions (in the extended sense) are studied, which is relevant to the problems mentioned above.     

A non-central banach valued limit theorem and its application

This note is devoted to the generalization of a limit theorem on sums of strongly dependent random variables (non-central limit theorem) to the case when the initial Gaussian sequence, functions of which are considered, assumes values in the Banach space of continuous functions on a compactum; special attention is given to the transition case, when the limit distribution changes by a jump.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova Akademii Nauk SSSR, Vol. 177, pp. 114–119, 1989.     

Limit cycles for a perturbation of a quadratic center with symmetry

To estimate the number of limit cycles appearing under a perturbation of a quadratic system that has a center with symmetry, we use the method of generalized Dulac functions. To this end, we reduce the perturbed system to a Liénard system with a small parameter, for which we construct a Dulac function depending on the parameter. This permits one to estimate the number of limit cycles in the perturbed system for all sufficiently small parameter values. We find the Dulac function by solving a linear programming problem. The suggested method is used to analyze four specific perturbed systems that globally have exactly three limit cycles [i.e., the limit cycle distribution 3 or (3, 0)] and two systems that have the limit cycle distribution (3, 1) (i.e., one nest around each of the two foci).     

Asymptotics of random convex polygonal lines

The present paper deals with the limit shape of random plane convex polygonal lines whose edges are independent and identically distributed, with finite first moment. The smoothness of a limit curve depends on some properties of the distribution. The limit curve is determined by the projection of the distribution to the unit circle. This correspondence between limit curves and measures on the unit circle is proved to be a bijection. The emphasis is on limit distributions of deviations of random polygonal lines from a limit curve. Normed differences of Minkowski support functions converge to a Gaussian limit process. The covariance of this process can be found in terms of the initial distribution. In the case of uniform distribution on the unit circle, a formula for the covariance is found. The main result is that a.s. sample functions of the limit process have continuous first derivative satisfying the Hölder condition of order a, for any fixed a with 0相似文献   

Coupled Continuous Time Random Maxima

Continuous Time Random Maxima (CTRM) are a generalization of classical extreme value theory: Instead of observing random events at regular intervals in time, the waiting times between the events are also random variables which have arbitrary distributions. In case that the waiting times between the events have infinite mean, the limit process that appears differs from the limit process that appears in the classical case. With a continuous mapping approach, we derive a limit theorem for the case that the waiting times and the subsequent events are dependent as well as for the case that the waiting times depend on the preceding events (in this case we speak of an Overshooting Continuous Time Random Maxima, abbr. OCTRM). We get the distribution functions of the limit processes and a formula for the Laplace transform in time of the CTRM and the OCTRM limit. With this formula we have another way to calculate the distribution functions of the limit processes, namely by inversion of the Laplace transform. Moreover, we present governing equations which in our case are time fractional differential equations whose solutions are the distribution functions of our limit processes.     

Wigner function of a relativistic particle in a time-dependent linear potential

We construct phase-space representations for a relativistic particle in both a constant and a time-dependent linear potential. We obtain explicit expressions for the Wigner distribution functions for these systems and find the correct nonrelativistic limit and free-particle limit for these functions. We derive the relativistic dynamical equation governing the time development of the Wigner distribution function and relativistic equation for the Wigner distribution function of stationary states and also calculate the amplitudes of transitions between energy states.     

An equivalent representation of the Brown-Resnick process

Brown and Resnick (1977) introduce a max-stable process that is obtained as a limit of maxima of independent Ornstein-Uhlenbeck processes. As shown in Kabluchko et al. (2009) this process is dissipative and it therefore admits a mixed moving maxima representation. We show that the distribution of the spectral functions in this representation equals a well-known diffusion, namely a standard Brownian motion with drift conditional on taking negative values only. This can be used for fast simulation methods.     

Almost sure central limit theorems on the Wiener space

In this paper, we study almost sure central limit theorems for sequences of functionals of general Gaussian fields. We apply our result to non-linear functions of stationary Gaussian sequences. We obtain almost sure central limit theorems for these non-linear functions when they converge in law to a normal distribution.     

On the concentration of the chromatic number of a random hypergraph

The problem on the limit distribution of the chromatic number of a random uniform hypergraph in the sparse case is studied. It is shown that, for most parameters values, the limit distribution of the chromatic number is concentrated at precisely one point, which can be found explicitly.     

Universal distribution of limit points

We consider sequences of functions that have in some sense a universal distribution of limit points of zeros in the complex plane. In particular, we prove that functions having universal approximation properties on compact sets with connected complement automatically have such a universal distribution of limit points. Moreover, in the case of sequences of derivatives, we show connections between this kind of universality and some rather old results of Edrei/MacLane and Pólya. Finally, we show the lineability of the set what we call Jentzsch-universal power series.     

Some Asymptotic Results for One-Sided Large Deviation Probabilities

We investigate the asymptotic behavior of the sum of independent real random variables. We assume that the random variables are not identically distributed but the average of distribution functions of these random variables is equivalent to some heavy-tailed limit distribution function. An example with Pareto law as limit function is given.     

Global asymptotic stability of a unique positive periodic solution for a discrete hematopoiesis model with unimodal production functions

The present paper is concerned with the global dynamics of a discrete hematopoiesis model. This model has several unimodal production functions, and periodic coefficients and periodic time delays whose periods are the same $$\omega \in {\mathbb {N}}$$. Since these production functions are unimodal, they have no monotonicity. The obtained result guarantees that this model has only one positive $$\omega $$-periodic solution and its periodic solution is globally asymptotically stable. To prove that the unique positive $$\omega $$-periodic solution is globally asymptotically stable, the difference between that the periodic solution and an arbitrary other is analyzed in detail. It is necessary to evaluate the upper and lower limit values of all the positive solutions to carry out the detailed analysis. Because the production functions have no monotonicity, it is not easy to evaluate the upper and lower limit values of the solutions. Two suitable examples are included to illustrate the main result. Numerical simulation is presented for one of them. The other example is based on the upper and lower limit values of red blood cells for healthy humans known from clinical laboratory tests, and clinical data obtained from clinical studies.     

Central Limit Theorem for Random Strict Partitions

We consider the set of all partitions of a number n into distinct summands (the so-called strict partitions) with the uniform distribution on it and study fluctuations of a random partition near its limit shape, for large n. The use of geometrical language allows us to state the problem in terms of the limit behavior of random step functions (Young diagrams). A central limit theorem for such functions is proven. Our method essentially uses the notion of large canonical ensemble of partitions. Bibliography: 7 titles.     

任意随机序列关于非齐次马氏链的随机和的一类随机偏差定理

In this paper, the notion of limit random logarithmic likelihood ratio of stochastic sequence, as a measure of dissimilarity between the joint distribution on measure P and the Markov distribution on measure Q, is introduced. A class of random approximation theorems for arbitrary stochastic dominated sequence are obtained by using the tools of generating functions and the tailed-probability generating functions.     

离散型与连续型分布互为对偶的根源

互为对偶的离散型分布与连续型分布,可以看作是由同一个函数——源函数产生的。源函数的正线性组合、乘积和负导数,仍然是源函数。源函数揭示了互为对偶的分布的分布函数之间的相互关系,并能用来求随机变量的数字特征、特征函数、概率母函数、分布的最大值和参数的极大似然估计.     

Methods for derivation of the stochastic Boltzmann hierarchy

We consider different methods for the derivation of the stochastic Boltzmann hierarchy corresponding to the stochastic dynamics that is the Boltzmann-Grad limit of the Hamiltonian dynamics of hard spheres. Solutions of the stochastic Boltzmann hierarchy are the Boltzmann-Grad limit of solutions of the BBGKY hierarchy of hard spheres in the entire phase space. A new concept of reduced distribution functions corresponding to the stochastic dynamics are introduced. They take into account the contribution of the hyperplanes of lower dimension where stochastic point particles interact with one another. The solutions of the Boltzmann equation coincide with one-particle distribution functions of the stochastic Boltzmann hierarchy and are represented by integrals over the hyperplanes where the stochastic point particles interact with one another.     

Continuous time trading of a small investor in a limit order market

We provide a mathematical framework to model continuous time trading of a small investor in limit order markets. We show how elementary strategies can be extended in a suitable way to general continuous time strategies containing orders with infinitely many different limit prices. The general limit buy order strategies are predictable processes with values in the set of nonincreasing demand functions. It turns out that our strategy set of limit and market orders is closed, but limit orders can turn into market orders when passing to the limit, and any element can be approximated by a sequence of elementary strategies.     

Extremes of Subexponential Shot Noise

Asymptotic behavior of extremes of shot noise in the case of monotone response functions with unbounded support and subexponentially distributed amplitudes is considered. It is assumed that the amplitude distribution belongs to the domain of attraction of a certain maximum-stable law. The limit distribution for maxima is obtained.     

On r-quick limit sets for empirical and related processes based on mixing random variables

The r-quick limit points of normalized sample paths and empirical distribution functions of mixing processes are characterized. An r-quick version of Bahadur-Kiefer-type representation for sample quantiles is established, which yields the r-quick limit points of quantile processes. These results are applied to linear functions of order statistics. Some results on r-quick convergence of certain Gaussian processes are also established.