Optimal rates for parameter estimation of stationary Gaussian processes

We study rates of convergence in central limit theorems for partial sums of polynomial functionals of general stationary and asymptotically stationary Gaussian sequences, using tools from analysis on Wiener space. In the quadratic case, thanks to newly developed optimal tools, we derive sharp results, i.e. upper and lower bounds of the same order, where the convergence rates are given explicitly in the Wasserstein distance via an analysis of the functionals’ absolute third moments. These results are tailored to the question of parameter estimation, which introduces a need to control variance convergence rates. We apply our result to study drift parameter estimation problems for some stochastic differential equations driven by fractional Brownian motion with fixed-time-step observations.     

Local properties of some Gaussian processes

The purpose of this paper is twofold. First we obtain exact formulae for the Hausdorff dimensions of the level sets and graph, and of the image of a fixed time set, for a Gaussian process with stationary increments and monotone incremental variance. Inequalities for these dimensions have been obtained by Kahane in the case where the process is the sum of a trigonometric series with random coefficients. Secondly we obtain some precise results for the brownian motion process. We consider when an image set has positive Lebesgue measure and when the zero set has positive capacity with respect to a given kernel. We show that certain conditions, which Kahane has shown to be sufficient to ensure these properties, are also necessary.     

Approximation of Gaussian measures generated by stationary processes. I

We consider local approximation of Gaussian measures generated by stationary processes, which are smooth functions of a parameter.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 79, pp. 44–66, 1978.     

Path properties of l_p-valued Gaussian random fields

General limit theorems are established for l~p-valued Gaussian random fields indexed by a multidimensional parameter,which contain both almost sure moduli of continuity and limits of large increments for the l~p-valued Gaussian random fields under(?)explicit conditions.     

Fractal properties of clusters generated by branching processes

An approach is developed to construction of random point distribution in 3-dimensional space based on the theory of branching processes. Correlation functions of all orders have been obtained in general form. The neutron and Lévy branching processes are numerically investigated. It is shown that fractal properties of the obtained distributions develop not only in the case of critical cascades but also in the case of subcritical, close to critical ones. In the last case, fractal properties appear in the limit region of distances. The difference between distributions generated by branching and nonbranching processes is discussed. Supported by the Russian State Committee on Higher Education (grant No. 95-0-3.1-23). Proceedings of the Seminar on Stability Problems for Stochastic Models, Hajdúszoboszló, Hungary, 1997, Part I.     

Path and semimartingale properties of chaos processes

The present paper characterizes various properties of chaos processes which in particular include processes where all time variables admit a Wiener chaos expansion of a fixed finite order. The main focus is on the semimartingale property, p$p$-variation and continuity. The general results obtained are finally used to characterize when a moving average is a semimartingale.     

Reproducing kernel Hilbert spaces and the law of the iterated logarithm for Gaussian processes

In this paper, we find the limit set of a sequence (2 log n)^?1/2 X _n (t), n≧3) of Gaussian processes in C [0,1], where the processes X _n (t) are defined on the same probability space and have the same distribution. Our result generalizes the theorems of Oodaira and Strassen, and we also apply it to obtain limit theorems for stationary Gaussian processes, moving averages of the type \(\int\limits_0^t {f\left( {t - s} \right)dW\left( s \right)} \) , where W(s) is the standard Wiener process, and other Gaussian processes. Using certain properties of the unit ball of the reproducing kernel Hubert space of X _n (t), we derive the usual law of the iterated logarithm for Gaussian processes. The case of multidimensional time is also considered.     

Spectral properties of processes derived from stationary Gaussian sequences

Many qualitative properties of the spectral measure of a stationary Gaussian sequence are spectral properties of the underlying shift transformation. This has implications in time series analysis.     

Decay parameter and related properties of n-type branching processes

We consider decay properties including the decay parameter, invariant measures, invariant vectors, and quasistationary distributions for n-type Markov branching processes on the basis of the 1-type Markov branching processes and 2-type Markov branching processes. Investigating such behavior is crucial in realizing life period of branching models. In this paper, some important properties of the generating functions for n-type Markov branching q-matrix are firstly investigated in detail. The exact value of the decay parameter λC of such model is given for the communicating class C = Zn+ \ 0. It is shown that this λC can be directly obtained from the generating functions of the corresponding q-matrix. Moreover, the λC -invariant measures/vectors and quasi-distributions of such processes are deeply considered. λC -invariant measures and quasi-stationary distributions for the process on C are presented.     

Path properties of an infinite system of Wiener processes

Let be a Poisson process on ^d of intensity and letW ₁(t),W ₂ (t),..., be a sequence of independent Wiener processes. LetW _i (t)=X _i +W _i (t) whereX ₁,X ₂,..., are the points of . Consider the processess(t)=#{i:X _i (t)1}. These and related processes are studied.     

Gaussian stochastic processes

The survey is devoted to works appearing in the last 3–5 years and pertaining mainly to local properties of the trajectories of Gaussian processes, the behavior of trajectories in the uniform metric, and properties of level sets. Some new results are also presented.     

Gaussian OS-positive processes

Summary Gaussian processes satisfying Osterwalder-Schrader positivity are studied. A representation of the (generalized) covariance function of an OS-positive process as the Laplace transform of an operator-valued probability measure is given. It is shown that every Gaussian OS-positive process has a unique Gaussian canonical Markov extension. An explicit application is made to the generalized free Euclidean fields.Partially supported by the National Science Foundation under grant MCS-76 06332     

Path properties of successive sample minima from stationary processes

Probability Theory and Related Fields -     

On some continuity and differentiability properties of paths of Gaussian processes

The following path properties of real separable Gaussian processes ξ with parameter set an arbitrary interval are established. At every fixed point the paths of ξ are continuous, or differentiable, with probability zero or one. If ξ is measurable, then with probability one its paths have essentially the same points of continuity and differentiability. If ξ is measurable and not mean square continuous or differentiable at every point, then with probability one its paths are almost nowhere continuous or differentiable, respectively. If ξ harmonizable or if it is mean square continuous with stationary increments, then its paths are absolutely continuous with probability one if and only if ξ is mean square differentiable; also mean square differentiability of ξ implies path differentiability with probability one at every fixed point. If ξ is mean square differentiable and stationary, then on every interval with probability one its paths are either differentiable everywhere or nondifferentiable on countable dense subsets. Also a class of harmonizable processes is determined for which of the following are true: (i) with probability one paths are either continuous or unbounded on every interval, and (ii) mean square differentiability implies that with probability one on every interval paths are either differentiable everywhere or nondifferentiable on countable dense subsets.     

Oscillation of Gaussian processes

The properties of the oscillations of Banach-valued Gaussian processes are investigated. The oscillations of several Gaussian sequences are computed. The obtained results are used for the investigation of the properties of the trajectories of one-dimensional Gaussian processes.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova Akademii Nauk SSSR, Vol. 177, pp. 92–97, 1989.     

lp-值Gauss过程样本轨道性质的注记

crements The a.s. sample path properties for ι^p-valued Gaussian processes with stationary increments under some more general conditions are established.     

Decay parameter and related properties of 2-type branching processes

We consider the decay parameter, invariant measures/vectors and quasi-stationary dis- tributions for 2-type Markov branching processes. Investigating such properties is crucial in realizing life period of branching models. In this paper, some important properties of the generating functions for 2-type Markov branching q-matrix are firstly investigated in detail. The exact value of the decay parameter λC of such model is given for the communicating class C = Z+2 \ 0. It is shown that this λC can be directly ...