Rates of convergence for the homogenization of fully nonlinear uniformly elliptic pde in random media

We establish a logarithmic-type rate of convergence for the homogenization of fully nonlinear uniformly elliptic second-order pde in strongly mixing media with similar, i.e., logarithmic, decorrelation rate. The proof consists of two major steps. The first, which is actually the only place in the paper where probability plays a role, establishes the rate for special (quadratic) data using the methodology developed by the authors and Wang to study the homogenization of nonlinear uniformly elliptic pde in general stationary ergodic random media. The second is a general argument, based on the new notion of δ-viscosity solutions which is introduced in this paper, that shows that rates known for quadratic can be extended to general data. As an application of this we also obtain here rates of convergence for the homogenization in periodic and almost periodic environments. The former is algebraic while the latter depends on the particular equation.     

Local linear estimator for stochastic diferential equations driven by α-stable Lvy motions

We study the local linear estimator for the drift coefcient of stochastic diferential equations driven byα-stable L′evy motions observed at discrete instants.Under regular conditions,we derive the weak consistency and central limit theorem of the estimator.Compared with Nadaraya-Watson estimator,the local linear estimator has a bias reduction whether the kernel function is symmetric or not under diferent schemes.A simulation study demonstrates that the local linear estimator performs better than Nadaraya-Watson estimator,especially on the boundary.     

Local linear estimator for stochastic differential equations driven by α-stable Lévy motions

We study the local linear estimator for the drift coefficient of stochastic differential equations driven by α-stable Lévy motions observed at discrete instants. Under regular conditions, we derive the weak consistency and central limit theorem of the estimator. Compared with Nadaraya-Watson estimator, the local linear estimator has a bias reduction whether the kernel function is symmetric or not under different schemes. A simulation study demonstrates that the local linear estimator performs better than Nadaraya-Watson estimator, especially on the boundary.     

On lacunary statistical convergence of order α

In this article, we introduce the concept of lacunary statistical convergence of order α of real number sequences and give some inclusion relations between the sets of lacunary statistical convergence of order α and strong N_θ^α(p)-summability. Furthermore, some relations between the spaces N_θ^αS_θ^α are examined.     

A least squares estimator for discretely observed Ornstein–Uhlenbeck processes driven by symmetric α-stable motions

We study the problem of parameter estimation for Ornstein–Uhlenbeck processes driven by symmetric α-stable motions, based on discrete observations. A least squares estimator is obtained by minimizing a contrast function based on the integral form of the process. Let h be the length of time interval between two consecutive observations. For both the case of fixed h and that of h → 0, consistencies and asymptotic distributions of the estimator are derived. Moreover, for both of the cases of h, the estimator has a higher order of convergence for the Ornstein–Uhlenbeck process driven by non-Gaussian α-stable motions (0 < α < 2) than for the process driven by the classical Gaussian case (α = 2).     

Complete convergence of weighted sums for arrays of rowwise φ-mixing random variables

In this paper, we establish the complete convergence and complete moment convergence of weighted sums for arrays of rowwise φ-mixing random variables, and the Baum-Katz-type result for arrays of rowwise φ-mixing random variables. As an application, the Marcinkiewicz-Zygmund type strong law of large numbers for sequences of φ-mixing random variables is obtained. We extend and complement the corresponding results of X. J. Wang, S. H. Hu (2012).     

Synchronization of systems of Marcus canonical equations driven by α-stable noises

Dynamical systems driven by Gaussian noises have been considered extensively in modeling, simulation and theory. However, complex systems in engineering and science are often subject to non-Gaussian fluctuations or uncertainties. A coupled dynamical system under non-Gaussian Lévy noise is considered. After discussing cocycle property, stationary orbits and random attractors, a synchronization phenomenon is shown to occur, when the drift terms of the coupled system satisfy certain dissipativity conditions. The synchronization result implies that coupled dynamical systems share a dynamical feature in certain asymptotic sense.     

Complete moment convergence for sequence of identically distributed φ-mixing random variables

In the paper we extend and generalize some results of complete moment convergence results （or the refinement of complete convergence） obtained by Chow [On the rate of moment complete convergence of sample sums and extremes. Bull. Inst. Math. Academia Sinica, 16, 177-201 （1988）] and Li ＆ Spataru [Refinement of convergence rates for tail probabilities. J. Theor. Probab., 18, 933-947 （2005）] to sequences of identically distributed φ-mixing random variables.     

Rates of convergence for the approximation of dual shift-invariant systems in ℓ2(ℤ)

A shift-invariant system is a collection of functions {g_m,n} of the form g_m,n(k)=g_m(k–an). Such systems play an important role in time-frequency analysis and digital signal processing. A principal problem is to find a dual system _m,n(k)=_m(k–an) such that each functionf can be written asf= f, _m,ngm,n. The mathematical theory usually addresses this problem in infinite dimensions (typically in L² () or ²()), whereas numerical methods have to operate with a finite-dimensional model. Exploiting the link between the frame operator and Laurent operators with matrix-valued symbol, we apply the finite section method to show that the dual functions obtained by solving a finite-dimensional problem converge to the dual functions of the original infinite-dimensional problem in ²(). For compactly supported g_{m, n} (FIR filter banks) we prove an exponential rate of convergence and derive explicit expressions for the involved constants. Further we investigate under which conditions one can replace the discrete model of the finite section method by the periodic discrete model, which is used in many numerical procedures. Again we provide explicit estimates for the speed of convergence. Some remarks on tight frames complete the paper.Part of this work was done while the author was a visitor at the Department of Statistics at the Stanford University.The author has been partially supported by Erwin-Schrödinger scholarship J01388-MAT of the Austrian Science foundation FWF.     

The convergence of the Rényi entropy of the normalized sums of IID random variables

We prove that the Rényi entropy of order $\alpha$ ($\alpha >1$) of the normalized sums of IID random variables with continuous differentiable density is convergent to the Rényi entropy of order $\alpha$ of the standard Gaussian distribution, and obtain the corresponding rates of convergence.     

Irreducibility and Asymptotics of Stochastic Burgers Equation Driven by α-stable Processes

Potential Analysis - The irreducibility, moderate deviation principle and ψ-uniformly exponential ergodicity with ψ(x) :=?1 + ∥x∥0 are proved for stochastic Burgers...     

A large deviation for occupation time of critical branching α-stable process

In this paper we establish a large deviation principle for the occupation times of critical branching α-stable processes for large dimensions d > 2α, by investigating two related nonlinear differential equations. Our result is an extension of Cox and Griffeath’s (in 1985) for branching Brownian motion for d > 4.     

A note on the rates of convergence for weighted sums of ρ * -mixing random variables

We discuss the rates of convergence for weighted sums of ρ ^? -mixing random variables. We solve an open problem posed by Sung [S.H. Sung, On the strong convergence for weighted sums of ρ ^? -mixing random variables, Stat. Pap., 54:773–781, 2013]. In addition, the two obtained lemmas in this paper improve the corresponding ones of Sung in the above-mentioned paper and [S.H. Sung, On the strong convergence for weighted sums of random variables, Stat. Pap., 52:447–454, 2011].     

Weak convergence of approximate solutions of stochastic equations with applications to random differential and integral equations ∗

In this paper, we considerably extend our earlier result about convergence in distribution of approximate solutions: of random operator equations, where the stochastic inputs and the underlying deterministic equation are simultaneously approximated. As a by-product, we obtain convergence results for approximate solutions of equations between spaces of probability measures. We apply our results to random Fredholm integral equations of the second kind and to a random [nbar]onlinear elliptic boundary value problem.     

Functional central limit theorem for super α-stable processes

A functional central limit theorem is proved for the centered occupation time process of the super α-stable processes in the finite dimensional distribution sense. For the intermediate dimensions α < d < 2α (0 < α ≤ 2), the limiting process is a Gaussian process, whose covariance is specified; for the critical dimension d= 2α and higher dimensions d < 2α, the limiting process is Brownian motion. Zhang Mei, Functional central limit theorem for the super-brownian motion with super-Brownian immigration, J. Theoret. Probab., to appear.     

Functional central limit theorem for super α-stable processes

A functional central limit theorem is proved for the centered occupation time process of the super α-stable processes in the finite dimensional distribution sense. For the intermediate dimensions α < d < 2α. (0 < α ≤ 2), the limiting process is a Gaussian process, whose covariance is specified; for the critical dimension d = 2α and higher dimensions d > 2α, the limiting process is Brownian motion.     

Sums of independent poisson subordinators and their connection with strictly α-stable processes of Ornstein-Uhlenbeck type

We describe a construction in which the discrete time of a sequence of independent, identically distributed random variables changes with the Poisson time. The Poisson time is independent of this sequence. The defined process with continuous time is called a random index process. We establish several properties of random index processes. We study asymptotics of sums of independent, identically distributed random index processes in the case where elements of the initial sequence have strictly α-stable distribution. By calculating characteristic functions we establish relationships of these sums with strictly α-stable processes of the Ornstein- Uhlenbeck type. Bibliography: 4 titles. Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 361, 2008, pp. 123–137.     

On convergence of random walks generated by compound Cox processes to Lévy processes

A functional limit theorem is proved establishing weak convergence of random walks generated by compound doubly stochastic Poisson processes to Lévy processes in the Skorokhod space. As corollaries, theorems are proved on convergence of random walks with jumps having finite variances to Lévy processes with mixed normal distributions, and in particular, to stable Lévy processes.