Limiting distributions of randomly accelerated motions

In this paper, the process {X(t); t>0}, representing the position of a uniformly accelerated particle (with Poisson-paced) changes of its acceleration, is studied. It is shown that the distribution ofX(t) (suitably normalized), conditionally on the numbern of changes of acceleration, tends in distribution to a normal variate asn goes to infinity. The asymptotic normality of the unconditional distribution ofX(t) for large values oft is also shown. The study of these limiting distributions is motivated by the difficulty of evaluating exactly the conditional and unconditional probability laws ofX(t). In fact, the results obtained in this paper permit us to give useful approximations of the probability distributions of the position of the particle. Dipartmento di Statistica, Probabilità Statistiche Applicate University of Rome “La Sapienza,” Italy. Published in Lietuvos Matematikos Rinkinys, Vol. 37, No. 3, pp. 295–308, July–September, 1997.     

The occupation time of Brownian motion in a ball

LetW be a Wiener process of dimensiond3, starting from 0, and letX(t) be the total time spent byW in the ball centered at 0 with radiust. We give an affirmative answer to a conjecture of Taylor and Tricot⁽¹⁶⁾ on the tail distribution ofX(t). Lévy's lower functions ofX(t) are characterized by an integral test.     

On almost sure noncentral limit theorems

LetX(t) be a fractional Brownian motion or Hermite process of indexH. SetX _m (t)=m ^–H X(mt), which we view as an element ofC[0, 1]. Let {x} denote a point mass at x. Then The corresponding results for certain partial sums in the domain of attraction toX(t) are shown to hold.     

Estimation of system reliability in Brownian stress-strength models based on sample paths

Reliability of many stochastic systems depends on uncertain stress and strength patterns that are time dependent. In this paper, we consider the problem of estimating the reliability of a system when bothX(t) andY(t) are assumed to be independent Brownian motion processes, whereX(t) is the system stress, andY(t) is the system strength, at timet.This research was partially supported by the Air-Force Office of Scientific Research Grants AFOSR-89-0402 and AFOSR-90-0402.     

On selection of the order of the spectral density model for a stationary process

Summary Let {X(t)} be a stationary process with mean zero and spectral densityg(x). We shall use akth order parametric spectral modelf _τ(k) (x) for this process. Without Gaussianity we can obtain an estiamte of τ(k), say ĝt(k), by maximizing the quasi-Gaussian likelihood of this model. We can then construct the best linear predictor ofX(t), which is computed on the basis of the estimated spectral densityf _ĝt(k) (x). An asymptotic lower bound of the mean square error of the estimated predictor is obtained. The bound is attained ifk is selected by Akaike's information criterion.     

α-self-similar Markov processes

Summary Let ((X(t)), P ^x) be an -self-similar isotropic Markov process on R ^d {0}. A representation of (X(t)), in terms of the radial and angular process which generalizes the skew product representation for Brownian motion is given.     

Hitting straight lines by compound Poisson process paths

In a recent article Mallows and Nair (1989,Ann. Inst. Statist. Math.,41, 1–8) determined the probability of intersectionP{X(t)=αt for somet≥0} between a compound Poisson process {X(t), t≥0} and a straight line through the origin. Using four different approaches (direct probabilistic, via differential equations and via Laplace transforms) we extend their results to obtain the probability of intersection between {X(t), t≥0} and arbitrary lines. Also, we display a relationship with the theory of Galton-Watson processes. Additional results concern the intersections with two (or more) parallel lines. Work done in part while these authors were visiting professors at the Indian Statistical Institute, Delhi Centre, New Delhi, 110016, India. This author's investigation was supported in part by the U. S. National Science Foundation Grant No. DMS-8504319. Our coauthor and friend Prem Singh Puri died on August 12, 1989. We dedicate our contribution to this paper to his memory.     

Numerical computation of an analytic singular value decomposition of a matrix valued function

Summary This paper extends the singular value decomposition to a path of matricesE(t). An analytic singular value decomposition of a path of matricesE(t) is an analytic path of factorizationsE(t)=X(t)S(t)Y(t) ^T whereX(t) andY(t) are orthogonal andS(t) is diagonal. To maintain differentiability the diagonal entries ofS(t) are allowed to be either positive or negative and to appear in any order. This paper investigates existence and uniqueness of analytic SVD's and develops an algorithm for computing them. We show that a real analytic pathE(t) always admits a real analytic SVD, a full-rank, smooth pathE(t) with distinct singular values admits a smooth SVD. We derive a differential equation for the left factor, develop Euler-like and extrapolated Euler-like numerical methods for approximating an analytic SVD and prove that the Euler-like method converges.Partial support received from SFB 343, Diskrete Strukturen in der Mathematik, Universität BielefeldPartial support received from FSP Mathematisierung, Universität BielefeldPartial support received from FSP Mathematisierung, Universität BielefeldPartial support received from National Science Foundation grant CCR-8820882. Some support was also received from the University of Kansas through International Travel Fund 560478 and General Research Allocations # 3758-20-0038 and #3692-20-0038.     

Upper classes for the increments of the fractional Wiener process

Summary LetX(t) be a fractional Wiener process, i.e., a centered Gaussian process on [0, ) with stationary increments and varianceEX ² (t)=t ², anda(t) a positive nondecreasing function witha(t)t. We investigate the a.s. asymptotic behaviour of the incrementsI(t, a (t))=max{X{u+a(t))–X(u): 0ut–a(t)} (and some others that are similarly defined) ast.     

Large Deviations of a Storage Process with Fractional Brownian Motion as Input

We study probabilities of large extremes of the storage process Y(t) = sup _t (X() - X(t) - c( - t)), where X(t) is the fractional Brownian motion. We derive asymptotic behavior of the maximum tail distribution for the process on fixed or slowly increased intervals by a reduction the problem to a large extremes problem for a Gaussian field.     

Functions of ann-dimensional Brownian motion that are Markovian

Letf be a continuous function fromR ⁿ toR and letX(t)=(X ₁ (t), …, X _n (t)) be a Brownian motion onR ⁿ . The explicit form off necessary in order to makef(X(t)) a Markov process is determined.     

Asymptotic behavior of unstable ARMA processes with application to least squares estimates of their parameters

A time series x(t), t≥1, is said to be an unstable ARMA process if x(t) satisfies an unstableARMA model such asx(t)=a_1x(t-1) a_2x(t-2) … a_8x(t-s) w(t)where w(t) is a stationary ARMA process; and the characteristic polynomial A(z)=1-a_1z-a_2z~2-…-a_3z~3 has all roots on the unit circle. Asymptotic behavior of sum form 1 to n (x~2(t)) will be studied by showing somerates of divergence of sum form 1 to n (x~2(t)). This kind of properties Will be used for getting the rates of convergenceof least squares estimates of parameters a_1, a_2,…, a_?     

Integrodifferential systems with infinitely many delays

Summary In this paper we consider the initial-value problems: (P ₁ )X(t)=(AX)(t) for t>0, X(0+)=I, X(t)=0 for t<0 and (P ₂ ) Y(t)=(QY)(t) for t>0, Y(0+)=I, Y(t)=0 for t<0, where A and Q are linear specified operators, I and0 — the identity and null matrices of order n, and X(t), Y(t) are unknown functions whose values are square matrices of order n. Sufficient conditions are established under which the problems (P ₁ ) and (P ₂ ) have the same unique solution, locally summable on the half-axis t ⩾0. Using this fact and some properties of the Laplace transform we find a new proof for the variation of constants formula given in[1, 2]. On the basis of this formula we derive certain results concerning a class of integrodifferential systems with infinite delay. Entrata in Redazione il 2 marzo 1977.     

On the Asymptotic Behaviour of the Solutions of a System of Functional Equations

In this paper we study the asymptotic behaviour of the solutions of the functional equation

Symmetrizations of Markov processes

Two methods for symmetrizing Markov processes are discussed. Letu ^a(x, y) be the potential density of a Lévy process on a compact Abelian groupG. A general condition is given that guarantees thatv(x, y)=u^a(x, y)+u^a(y, x) is the potential density of a symmetric Lévy process onG. The second method arises by considering the linear space of one-potentialsU ¹ f, withf inL ², endowed with the inner product (U ¹ f,U ¹ g)=fU ¹ g+gU ¹ f. If the semigroup ofX(t) is normal, then the completionH of this space is the Dirichlet space of a symmetric processY(t). A set that is semipolar forX(t) is polar forY(t).     

Approximating the coefficients in semilinear stochastic partial differential equations

We investigate, in the setting of UMD Banach spaces E, the continuous dependence on the data A, F, G and ξ of mild solutions of semilinear stochastic evolution equations with multiplicative noise of the form

Chaotic Tensor Product Semigroups

Suppose {G₁(t)}_{t ≥ 0} and {G₂(t)_{t ≥ 0} be two semigroups on an infinite dimensional separable reflexive Banach space X. In this paper we give sufficient conditions for tensor product semigroup G(t): X → G₂(t)X G₁(t) to become chaotic in L with the strong operator topology and chaotic in the ideal of compact operators on X with the norm operator topology.     

Discrete Periodic Sampling with Jitter and Almost Periodically Correlated Processes

The zero-mean process is said to be almost periodically correlated whenever its shifted covariance kernel is almost periodic in t uniformly with respect to . Then it admits a Fourier–Bohr decomposition: . This paper deals with the estimation of the spectral covariance a(λ,τ) from a discrete time observation of the process , when jitter and delay phenomena are present in conjunction with periodic sampling. Under mixing conditions, we establish the consistency and the asymptotic normality of empirical estimators as the sampling time step tends to 0 and the sampling period tends to infinity.      

The Generalized Multifractional Field: A Nice Tool for the Study of the Generalized Multifractional Brownian Motion

The Generalized Multifractional Brownian Motion (GMBM) is a continuous Gaussian process {X(t)}_{t ? [0,1]}\{X(t)\}_{t\in [0,1]} that extends the classical Fractional Brownian Motion (FBM) and the Multifractional Brownian Motion (MBM) [15, 4, 1, 1]. Its main interest is that, its Hölder regularity can change widely from point to point. In this article we introduce the Generalized Multifractional Field (GMF), a continuous Gaussian field {Y(x,y)}_{(x,y) ? [0,1] ²}\{Y(x,y)\}_{(x,y)\in [0,1]^{\,2}} that satisfies for every tt, X(t)=Y(t,t)X(t)=Y(t,t). Then, we give a wavelet decomposition of YY and using this nice decomposition, we show that YY is b\beta-Hölder in yy, uniformly in xx. Generally speaking this result seems to be quite important for the study of the GMBM. In this article, it will allow us to determine, without any restriction, its pointwise, almost sure, Hölder exponent and to prove that two GMBM's with the same Hölder regularity differ by a "smoother' process.     

On the infimum of the local time of a Wiener process

Let {W(t), t0} be a standard Wiener process, and let L(x, t) be its jointly continuous local time. Define