On the asymptotics of supremum distribution for some iterated processes

In this paper, we study the asymptotic behavior of supremum distribution of some classes of iterated stochastic processes \(\{X(Y(t)) : t \in [0, \infty )\}\), where \(\{X(t) : t \in \mathbb {R} \}\) is a centered Gaussian process and \(\{Y(t): t \in [0, \infty )\}\) is an independent of {X(t)} stochastic process with a.s. continuous sample paths. In particular, the asymptotic behavior of \(\mathbb {P}(\sup _{s\in [0,T]} X(Y (s)) > u)\) as \(u \to \infty \), where T>0, as well as \(\lim _{u\to \infty } \mathbb {P}(\sup _{s\in [0,h(u)]} X(Y (s)) > u)\), for some suitably chosen function h(u) are analyzed. As an illustration, we study the asymptotic behavior of the supremum distribution of iterated fractional Brownian motion process.     

Estimation of cusp location of stochastic processes: a survey

We present a review of some recent results on estimation of location parameter for several models of observations with cusp-type singularity at the change point. We suppose that the cusp-type models fit better to the real phenomena described usually by change point models. The list of models includes Gaussian, inhomogeneous Poisson, ergodic diffusion processes, time series and the classical case of i.i.d. observations. We describe the properties of the maximum likelihood and Bayes estimators under some asymptotic assumptions. The asymptotic efficiency of estimators are discussed as well and the results of some numerical simulations are presented. We provide some heuristic arguments which demonstrate the convergence of log-likelihood ratios in the models under consideration to the fractional Brownian motion.     

Generators of some non-commutative stochastic processes

A fundamental result of Biane (Math Z 227:143–174, 1998) states that a process with freely independent increments has the Markov property, but that there are two kinds of free Lévy processes: the first kind has stationary increments, while the second kind has stationary transition operators. We show that a process of the first kind (with mean zero and finite variance) has the same transition operators as the free Brownian motion with appropriate initial conditions, while a process of the second kind has the same transition operators as a monotone Lévy process. We compute an explicit formula for the generators of these families of transition operators, in terms of singular integral operators, and prove that this formula holds on a fairly large domain. We also compute the generators for the $q$ -Brownian motion, and for the two-state free Brownian motions.     

Spline approximation of some stochastic processes

Translated from Issledovaniya po Prikladnoi Matematike, No. 10, pp. 206–211, 1984.     

Spectral multiplicity of some stochastic processes

In this paper we consider the connection between the canonical and the weak-canonical representations for the given second-order stochastic process in a separable Hilbert space and we extend a well-known theorem of H. Cramer concerning sufficient conditions for a process to be of multiplicity one.

     

Study of dependence for some stochastic processes: Symbolic Markov copulae

We study dependence between components of multivariate (nice Feller) Markov processes: what conditions need to be satisfied by a multivariate Markov process so that its components are Markovian with respect to the filtration of the entire process and such that they follow prescribed laws? To answer this question, we introduce a symbolic approach, which is rooted in the concept of pseudo-differential operator (PDO). We investigate connections between dependence, in the sense described above, and the PDOs. In particular, we study the problem of constructing a multivariate nice Feller process with given marginal laws in terms of symbols of the related PDOs. This approach leads to relatively simple conditions, which provide solutions to this problem.     

On some properties ofJ-convex stochastic processes

Summary We present some results on the differentiability of convex stochastic processes. Furthermore, the stochastic version of a theorem onJ-convex functions majorized byJ-concave functions is given.     

The asymptotics of some evolution equations

We consider the contributions to the asymptotics of the function exp (T), t± , caused by the spectral singularity of a nonselfadjoint Sturm-Liouville operator T in the space L²(0, ). The potential is assumed to decrease like a power of the variable, and the element belongs to the domain of definition of both the operator T and a certain extension of it to increasing functions.Translated fromMatematichni Metodi ta Fiziko-Mekhanichni Polya, Vol. 40, No. 4, 1997, pp. 75–85.     

Large-time asymptotics for an uncorrelated stochastic volatility model

We derive a large-time large deviation principle for the log stock price under an uncorrelated stochastic volatility model. For this we use a Donsker-Varadhan-type large deviation principle for the occupation measure of the Ornstein-Uhlenbeck process, combined with a simple application of the contraction principle and exponential tightness.     

Properties of some stochastic processes arising in estimation theory

Sample functions of random processes are used to make inferences about the properties of estimators. In particular, it is proved that optimal equivariant sequential estimation designs with stopping timet such that Εt n ₁, are better than optimal equivariant estimation of the location parameter for samples of sizen, with largen. It is assumed that the density has cusps of first or second kind.     

The efficiency criteria problem for stochastic processes

This paper is concerned with the problem of finding a suitable (asymptotic) efficiency criterion for inference concerning parameters of stochastic processes. Special attention is aid to conditional exponential families of stochastic processes and to three tests based on the maximum likelihood estimate as well as to the likelihood ratio test. A contiguity calculation is used to show that a previously suggested criterion is inadequate and itself provides a partial solution to the problem. A heuristic argument is also put forward to support a proposition implying the optimality of the maximum likelihood estimate in a certain sense. Two examples which illustrate the theory are discussed.     

Small time asymptotics of diffusion processes

We establish the short-time asymptotic behaviour of the Markovian semigroups associated with strongly local Dirichlet forms under very general hypotheses. Our results apply to a wide class of strongly elliptic, subelliptic and degenerate elliptic operators. In the degenerate case the asymptotics incorporate possible non-ergodicity.     

The uniform convergence and precise asymptotics of generalized stochastic order statistics

Let X(i,n,m,k), i=1,…,n, be generalized order statistics based on F. For fixed r∈N, and a suitable counting process N(t), t>0, we mainly discuss the precise asymptotic of the generalized stochastic order statistics X(N(n)−r+1,N(n),m,k). It not only makes the results of Yan, Wang and Cheng [J.G. Yan, Y.B. Wang, F.Y. Cheng, Precise asymptotics for order statistics of a non-random sample and a random sample, J. Systems Sci. Math. Sci. 26 (2) (2006) 237-244] as the special case of our result, and presents many groups of weighted functions and boundary functions, but also permits a unified approach to several models of ordered random variables.     

Invariant measures and asymptotics for some skew products

For certain group extensions of uniquely ergodic transformations, we identify all locally finite, ergodic, invariant measures. These are Maharam type measures. We also establish the asymptotic behaviour for these group extensions proving logarithmic ergodic theorems, and bounded rational ergodicity.