On random observation processes for stochastic approximation

Summary In [1] a new procedure is given to estimate the root of a regression equation. The purposes of this paper are to extend the Lemma 1 in [1] and to give a process involving a randomly determined sequence of observations for finding thermaximum of a regression function. The process is similar to that of [1]. Kiefer and Wolfowitz [2] gave a stochastic approximation procedure for the latter purpose. Their process needs the condition of the unimodality of the regression function which is not required for our case.     

On random coefficient INAR(1) processes

The random coefficient integer-valued autoregressive process was introduced by Zheng,Basawa,and Datta in 2007.In this paper we study the asymptotic behavior of this model(in particular,weak limits of extreme values and the growth rate of partial sums) in the case where the additive term in the underlying random linear recursion belongs to the domain of attraction of a stable law.     

On infinitesimal σ-fields generated by random processes

It is proved that the infinitesimal look-ahead and look-back σ-fields of a random process disagree at atmost countably many time instants.     

On small deviation probabilities for certain iterated random processes

The asymptotic behavior of small deviation probabilities for some iterated random processes is investigated. It is shown that, under certain conditions, iterated and noniterated processes have logarithmic asymptotics of the same character; otherwise, these asymptotics may differ substantially. Some iterated Gaussian processes are considered as an example.     

On the safety stock problem for random delivery processes

Inventory models are considered in which the delivery of an order occurs not on one occasion but at random moments of a period in random parts. We give two extensions of the reliability type inventory model of A. Prékopa. In this model a known constant demand rate is assumed and a simple approximate formula is given for the initial stock of the order period which serves as safety stock and ensures a continuous supply during the whole order period on a prescribed probability level. This formula is widely used in practice for safety stock planning in the case when deliveries in random parts occur.We formulate a generalized version of the random delivery process and derive the exact solution of the safety stock which can be applied also for the previous model. In the second model a random demand rate is considered together with a random delivery process. An exact solution method and a simple approximate formula for the safety stock will be discussed. We have experiences in the application of these models both in a steel works and a textile factory in Hungary.     

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.     

Increasing permutations of random processes

The paper is concerned with an application of limit theorems to the study of increasing permutations of stable random processes. By the increasing permutation of a function is meant the nondecreasing function with the same distribution. The trajectories of a random process may be approximated by step-functions, and then the continuity of the increasing permutation operator permits one to apply the Skorokhod invariance principle to obtain the distribution of the random process. The distribution function and the expected value of the increasing permutation of a stable random process are given explicitly. Also the univariate distributions of the increasing permutation of the Cauchy process are obtained. In various normed spaces the images of the unit balls with respect to the operator of increasing permutation are described. A separate section is devoted to the increasing permutations of higher-dimensional processes. Bibliography: 5 titles. Translated fromZapiski Nauchnykh Seminarov POMI, Vol. 216, 1994, pp. 62–75. Translated by A. Sudakov.     

On estimates of the bispectral density of certain models of random processes

Estimates are given for the bispectral densities of stationary random processes. Two models of stationary random processes with discrete time are considered. It is shown that for both models conditions hold under which one can construct a consistent estimate of the bispectral density.Translated fromTeoriya Sluchaínykh Protsessov, Vol. 14, pp. 3–7, 1986.     

On deviations between empirical and quantile processes for mixing random variables

Let {X_n} be a strictly stationary φ-mixing process with Σ_j=1^∞ φ^1/2(j) < ∞. It is shown in the paper that if X₁ is uniformly distributed on the unit interval, then, for any t [0, 1], |F_n⁻¹(t) − t + F_n(t) − t| = O(n^−3/4(log log n)^3/4) a.s. and sup_0≤t≤1 |F_n⁻¹(t) − t + F_n(t) − t| = (O(n^−3/4(log n)^1/2(log log n)^1/4) a.s., where F_n and F_n⁻¹(t) denote the sample distribution function and tth sample quantile, respectively. In case {X_n} is strong mixing with exponentially decaying mixing coefficients, it is shown that, for any t [0, 1], |F_n⁻¹(t) − t + F_n(t) − t| = O(n^−3/4(log n)^1/2(log log n)^3/4) a.s. and sup_0≤t≤1 |F_n⁻¹(t) − t + F_n(t) − t| = O(n^−3/4(log n)(log log n)^1/4) a.s. The results are further extended to general distributions, including some nonregular cases, when the underlying distribution function is not differentiable. The results for φ-mixing processes give the sharpest possible orders in view of the corresponding results of Kiefer for independent random variables.     

On the uniform convergence of wavelet expansions of random processes from Orlicz spaces of random variables. II

We establish conditions under which wavelet expansions of random processes from Orlicz spaces of random variables converge uniformly with probability one on a bounded interval. Translated from Ukrains'kyi Matematychnyi Zhurnal, Vol. 60, No. 6, pp. 759–775, June, 2008.     

On the uniform convergence of wavelet expansions of random processes from Orlicz spaces of random variables. I

We establish conditions under which there exists a function c(t) > 0 such that {fx1850-01}, where X(t) is a random process from an Orlicz space of random variables. We obtain estimates for the probabilities {fx1850-02}. __________ Translated from Ukrains’kyi Matematychnyi Zhurnal, Vol. 59, No. 12, pp. 1647–1660, December, 2007.     

Jump-diffusion processes in random environments

In this paper we investigate jump-diffusion processes in random environments which are given as the weak solutions of SDEs. We formulate conditions ensuring existence and uniqueness in law of solutions. We investigate the Markov property. To prove uniqueness we solve a general martingale problem for càdlàg processes. This result is of independent interest. Application of our results to generalized exponential Lévy model are present in the last section.