Convergence rates in strong ergodicity for Markov processes

A coupling method is used to obtain the explicit upper and lower bounds for convergence rates in strong ergodicity for Markov processes. For one-dimensional diffusion processes and birth–death processes, these bounds are sharp in the sense that the upper one and the lower one only differ in a constant.     

长程相依过程关于矩完全收敛的精确渐近性质

令X_t=∑_k=0^∞a_kε_t-k为一滑动平均过程,其中ε_k为均值为零的独立同分布随机变量序列,{a_k,k≥0}为满足条件a_k～k^-αl（k）的实数序列,其中l（k）为缓变函数.当1/2<α<1时,X_t为一长程相依过程,如分数积分过程等.该文得到了长程相依过程X_t关于一类矩完全收敛的精确渐近性质,此结果可直接得到X_t完全收敛的精确渐近性质.     

Convergence rates for probabilities of moderate deviations for moving average processes

The present paper first shows that, without any dependent structure assumptions for a sequence of random variables, the refined results of the complete convergence for the sequence is equivalent to the corresponding complete moment convergence of the sequence. Then this paper investigates the convergence rates and refined convergence rates （or complete moment convergence） for probabilities of moderate deviations of moving average processes. The results in this paper extend and generalize some well-known results.     

RATE OF CONVERGENCE FOR MULTIPLE CHANGEPOINTS ESTIMATION OF MOVING-AVERAGE PROCESSES

In this paper, the least square estimator in the problem of multiple change points estimation is studied. Here, the moving-average processes of ALNQD sequence in the mean shifts are discussed. When the number of change points is known, the rate of convergence of change-points estimation is derived. The result is also true for p-mixing, φ-mixing, a-mixing, associated and negatively associated sequences under suitable conditions.     

Convergence rates in density estimation for data from infinite-order moving average processes

Summary The effect of long-range dependence in nonparametric probability density estimation is investigated under the assumption that the observed data are a sample from a stationary, infinite-order moving average process. It is shown that to first order, the mean integrated squared error (MISE) of a kernel estimator for moving average data may be expanded as the sum of MISE of the kernel estimator for a same-sizerandom sample, plus a term proportional to the variance of the moving average sample mean. The latter term does not depend on bandwidth, and so imposes a ceiling on the convergence rate of a kernel estimator regardless of how bandwidth is chosen. This ceiling can be quite significant in the case of long-range dependence. We show thatall density estimators have the convergence rate ceiling possessed by kernel estimators.The research of Dr. Hart was done while he was visiting the Australian National University, and was supported in part by ONR Contract N00014-85-K-0723     

Convergence of diffusion processes

For stochastic diffusion equations with coefficients depending on a parameter, necessary and sufficient conditions of the weak convergence of solutions to the solution of a stochastic diffusion equation are obtained.Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 44, No. 2, pp. 284–289, February, 1992.     

Convergence in distribution and Skorokhod Convergence for the general theory of processes

Summary This paper proves some Skorokhod Convergence Theorems for processes with filtration. Roughly, these are theorems which say that if a family of processes with filtration (X ⁿ , ⁿ ),n, converges in distribution in a suitable sense, then there exists a family of equivalent processes (Y ⁿ , ⁿ ),n, which converges almost surely. The notion of equivalence used is that of adapted distribution, which guarantees that each (X ⁿ , ⁿ ) has the same stochastic properties as (X ⁿ , ⁿ ), with respect to its filtration, such as the martingale property or the Markov property. The appropriate notion of convergence in distribution is convergence in adapted distribution, which is developed in the paper. Fortunately, any tight sequence of processes has a subsequence which converges in adapted distribution. For discrete time processes, (Y ⁿ , ⁿ ),n, and their limit (Y, ) may be taken as all having the same fixed filtration ⁿ =. In the continuous time case, theY ⁿ , ⁿ may require different filtrations ⁿ , which converge to. To handle this, convergence of filtrations is defined and its theory developed.During part of the time this work was in progress, it was supported by an NSERC operating grant, and the author was an NSERC University Research Fellow. The author wishes to thank the Steklov Mathematical Institute of the Soviet Academy of Sciences for its hospitality while the principle research in this paper was being begun, A.N. Shiryaev and P.C. Greenwood, who made the author's visit there possible, and Ján Miná for his hospitality while that research was being finished. We thank the referee who suggested the results in Sect. 12     

Convergence rates of cascade algorithms

We consider solutions of a refinement equation of the form

where is a finitely supported sequence called the refinement mask. Associated with the mask is a linear operator defined on by . This paper is concerned with the convergence of the cascade algorithm associated with , i.e., the convergence of the sequence in the -norm.

Our main result gives estimates for the convergence rate of the cascade algorithm. Let be the normalized solution of the above refinement equation with the dilation matrix being isotropic. Suppose lies in the Lipschitz space , where 0$"> and . Under appropriate conditions on , the following estimate will be established:

where and is a constant. In particular, we confirm a conjecture of A. Ron on convergence of cascade algorithms.

     

Precise asymptotics in complete moment convergence of the associated counting process

Let be the associated counting process. In this paper, we prove the precise asymptotics in complete moment convergence of the associated counting process generated by i.i.d. random variables.     

Convergence of Bellman-Harris branching processes

A limit theorem regarding the convergence of a sequence of normalized Bellman-Harris processes to an Jirina process (a branching process with a continuous phase space) is obtained.Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 42, No. 6, pp. 827–832, June, 1990.     

Convergence rates for intermediate problems

Convergence rate estimates are derived for a variant of Aronszajn-type intermediate problems that is both computationally feasible and known to be convergent for problems with nontrivial essential spectrum. Implementation of these derived bounds is discussed in general and illustrated on differential eigenvalue problems. Convergence rates are derived for the commonly used method of simple truncation.The work of the first author was partially supported by AFOSR Grant 84-0326. The work of the second author was partially supported by NSF Grant MCS-8301402     

Convergence rates for Kaczmarz-type algorithms

By making use of the normal and skew-Hermitian splitting (NSS) method as the inner solver for the modified Newton method, we establish a class of modified Newton-NSS method for solving large sparse systems of nonlinear equations with positive definite Jacobian matrices at the solution points. Under proper conditions, the local convergence theorem is proved. Furthermore, the successive-overrelaxation (SOR) technique has been proved quite successfully in accelerating the convergence rate of the NSS or the Hermitian and skew-Hermitian splitting (HSS) iteration method, so we employ the SOR method in the NSS iteration, and we get a new method, which is called modified Newton SNSS method. Numerical results are given to examine its feasibility and effectiveness.     

Convergence of iterative processes in nonlinear optimization

We discuss here two questions related to the convergence of a class of iterative processes to find the minimum point of convex functionals. The iterative process is first viewed as arising from a sequence of contraction mappings whose contraction constants approach one. The rate of convergence of the process is then discussed in terms of these constants. We then study the convergence of gradient-type methods when they are subject to random errors. Sufficient conditions are obtained for various types of probabilistic convergence.     

Convergence of processes of step sums on mixing processes

We prove a limit theorem on the convergence of nonhomogeneous centered processes of step sums, constructed on random mixing sequences, to a process with independent increments. We also prove a invariance-principle type theorem for schemes of summation of functionals on random mixing sequences.Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 42, No. 5, pp. 716–721, May, 1990.