Dimension Free and Infinite Variance Tail Estimates on Poisson Space

Concentration inequalities are obtained on Poisson space, for random functionals with finite or infinite variance. In particular, dimension free tail estimates and exponential integrability results are given for the Euclidean norm of vectors of independent functionals. In the finite variance case these results are applied to infinitely divisible random variables such as quadratic Wiener functionals, including Lévy’s stochastic area and the square norm of Brownian paths. In the infinite variance case, various tail estimates such as stable ones are also presented.      

An extension of a result of Burdzy and Lawler

Summary It is shown that for all mean zero, finite variance random walks, the critical non-intersection exponents are equal to those for Brownian motion. The method uses the local time of intersection.Research supported by grants from NSA and NSF     

The Hitting Distribution of a Line Segment for Two-Dimensional Random Walks

Asymptotic estimates of the hitting distribution of a long segment on the real axis for two-dimensional random walks on \(\mathbf{Z}^2\) of zero mean and finite variances are obtained: Some are general and exhibit its apparent similarity to the corresponding Brownian density, while others are so detailed as to involve certain characteristics of the random walk.     

PROBABILISTIC OSTROWSKI TYPE INEQUALITIES

New very general univariate and multivariate probabilistic Ostrowski type inequalities are established, involving ‖·‖_∞ and ‖·‖ _p , p≥1 norms of probability density functions. Some of these inequalities provide pointwise estimates to the error of probability distribution function from the expectation of some simple function of the engaged random variable. Other inequalities give upper bounds for the expectation and variance of a random variable. All are done over finite domains. At the end are given applications, especially for the Beta random variable.     

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??Let {X_n;\,n\ge1} be a sequence of strictly stationary \rho-mixing random variables with zero mean and finite variance. Using the weak convergence theorem and probability inequalities of \rho-mixing sequence, under some proper conditions, we obtained general laws of precise asymptotics for partial sums of \rho-mixing sequence.     

\rho-mixing sequence| partial sums| precise asymptotic

Let {X_n;\,n\ge1} be a sequence of strictly stationary \rho-mixing random variables with zero mean and finite variance. Using the weak convergence theorem and probability inequalities of \rho-mixing sequence, under some proper conditions, we obtained general laws of precise asymptotics for partial sums of \rho-mixing sequence.     

Extremal first passage times for trees

For random walks associated with trees with probability zero of staying at any vertex, we develop explicit graph theoretic formulas for the mean first passage times between states, we give lower and upper bounds for the entries of the mean first passage matrix E, and we characterize the cases of equality in these bounds. We also consider the variance of the first return time to a state and we find those trees which maximize the variance and those trees which minimize the variance. As may be expected, the trees which provide extremal behavior are given by paths and stars.     

Nash inequalities for finite Markov chains

This paper develops bounds on the rate of decay of powers of Markov kernels on finite state spaces. These are combined with eigenvalue estimates to give good bounds on the rate of convergence to stationarity for finite Markov chains whose underlying graph has moderate volume growth. Roughly, for such chains, order (diameter) steps are necessary and suffice to reach stationarity. We consider local Poincaré inequalities and use them to prove Nash inequalities. These are bounds onl ₂-norms in terms of Dirichlet forms andl ₁-norms which yield decay rates for iterates of the kernel. This method is adapted from arguments developed by a number of authors in the context of partial differential equations and, later, in the study of random walks on infinite graphs. The main results do not require reversibility.     

Characterization of sub‐Gaussian heat kernel estimates on strongly recurrent graphs

Sub‐Gaussian estimates for random walks are typical of fractal graphs. We characterize them in the strongly recurrent case, in terms of resistance estimates only, without assuming elliptic Harnack inequalities. © 2005 Wiley Periodicals, Inc.     

Positive Discrete Spectrum of the Evolutionary Operator of Supercritical Branching Walks with Heavy Tails

We consider a continuous-time symmetric supercritical branching random walk on a multidimensional lattice with a finite set of the particle generation centres, i.e. branching sources. The main object of study is the evolutionary operator for the mean number of particles both at an arbitrary point and on the entire lattice. The existence of positive eigenvalues in the spectrum of an evolutionary operator results in an exponential growth of the number of particles in branching random walks, called supercritical in the such case. For supercritical branching random walks, it is shown that the amount of positive eigenvalues of the evolutionary operator, counting their multiplicity, does not exceed the amount of branching sources on the lattice, while the maximal of these eigenvalues is always simple. We demonstrate that the appearance of multiple lower eigenvalues in the spectrum of the evolutionary operator can be caused by a kind of ‘symmetry’ in the spatial configuration of branching sources. The presented results are based on Green’s function representation of transition probabilities of an underlying random walk and cover not only the case of the finite variance of jumps but also a less studied case of infinite variance of jumps.     

On mixing of certain random walks, cutoff phenomenon and sharp threshold of random matroid processes

In this paper we define and analyze convergence of the geometric random walks, which are certain random walks on vector spaces over finite fields. We show that the behavior of such walks is given by certain random matroid processes. In particular, the mixing time is given by the expected stopping time, and the cutoff is equivalent to sharp threshold. We also discuss some random geometric random walks as well as some examples and symmetric cases.     

The hitting distributions of a half real line for two-dimensional random walks

For every two-dimensional random walk on the square lattice Z ² having zero mean and finite variance we obtain fine asymptotic estimates of the probability that the walk hits the negative real line for the first time at a site (s,0), when it is started at a site far from both (0,s) and the origin.     

A new procedure for the estimation of variance components

For the estimation of variance components in the one way random effects models, we propose some estimators which avoid negative and zero estimates of the variance component, a well-known problem with customary estimators such as the maximum likelihood or the restricted maximum likelihood estimators. The proposed estimators are shown to have lower mean squared error than customary estimators over a large range of the parameter space. This is also exhibited in a Monte Carlo study. Extensions of the proposed procedure to more complex situations are also discussed.     

Two-sided estimates for solutions to the Cauchy problem for Wazewski linear differential systems with delay

The Cauchy problem is considered for Wazewski linear differential systems with finite delay. The right-hand sides of systems contain nonnegative matrices and diagonal matrices with negative diagonal entries. The initial data are nonnegative functions. The matrices in equations are such that the zero solution is asymptotically stable. Two-sided estimates for solutions to the Cauchy problem are constructed with the use of the method of monotone operators and the properties of nonsingular M-matrices. The estimates from below and above are zero and exponential functions with parameters determined by solutions to some auxiliary inequalities and equations. Some estimates for solutions to several particular problems are constructed.     

Stochastic compactness of distributions of sums of independent random variables with finite variances<Superscript>∗</Superscript>

We consider sums of independent random variables within the scheme of series. We focus on the case where every summand has a zero mean and finite variance and sums have unit variances. We obtain a criterion of stochastic compactness (defined by W. Feller) for sequences of distributions of such sums. The condition of uniform asymptotic negligibility of summands is not supposed.     

Some optimal bounds in the central limit theorem using zero biasing

The convergence rate in the central limit theorem (CLT) is investigated in terms of a wide class of probability metrics. Namely, optimal estimates for the proximity between a probability distribution and its zero bias transformation are derived. These new inequalities allow one to establish optimal rates of convergence in the CLT for sums of independent random variables with finite moments of order s, s∈(2,3], in terms of ideal metrics introduced by V.M. Zolotarev.     

变系数混合效应模型

本文研究了下列变系数混合效应模型: $y_{ij}=z_{ij}^{\tau}b_i+x_{ij}^{\tau}\beta(w_{ij}) +\xe_{ij},\;i=1,\cdots,m;\;j=1,\cdots,n_i$, 其中$b_i$为i.i.d.期望为$\xt$, 协方差阵为$\xs^2_bI_q$的随机效应向量, $\xe_{ij}$是i.i.d.期望为零, 具有有限方差的随机误差. 文中我们不仅给出了函数系数向量$\xb(\cdot)$的局部多项式估计, 同时给出了随机效应期望、方差和随机误差方差的估计, 并给出了这些估计量的渐进正态性和相合性, 研究结果表明了这些估计量的可靠性.     

A General Darling–Erdős Theorem in Euclidean Space

We provide an improved version of the Darling–Erd?s theorem for sums of i.i.d. random variables with mean zero and finite variance. We extend this result to multidimensional random vectors. Our proof is based on a new strong invariance principle in this setting which has other applications as well such as an integral test refinement of the multidimensional Hartman–Wintner LIL. We also identify a borderline situation where one has weak convergence to a shifted version of the standard limiting distribution in the classical Darling–Erd?s theorem.     

Logarithmic Sobolev,isoperimetry and transport inequalities on graphs

In this paper,we study some functional inequalities(such as Poincaré inequality,logarithmic Sobolev inequality,generalized Cheeger isoperimetric inequality,transportation-information inequality and transportation-entropy inequality) for reversible nearest-neighbor Markov processes on connected finite graphs by means of(random) path method.We provide estimates of the involved constants.