On the Vector Process Obtained by Iterated Integration of the Telegraph Signal

We analyse the vector process (X ₀(t), X ₁(t),...,X _n(t), t > 0) where , and X ₀(t) is the o two-valued telegraph process.In particular, the hyperbolic equations governing the joint distributions of the process are derived and analysed.Special care is given to the case of the process (X ₀(t), X ₁(t), X ₂(t), t > 0) representing a randomly accelerated motion where some explicit results on the probability distribution are derived.     

Enveloppes convexes des processus gaussiens

Let X={X(t), t[0,1]} be a process on [0,1] and V_X=Conv{(t,x)t[0,1], x=X(t)} be the convex hull of its path.The structure of the set ext(V_X) of extreme points of V_X is studied. For a Gaussian process X with stationary increments it is proved that:

• The set ext(V_X) is negligible if X is non-differentiable.

• If X is absolutely continuous process and its derivative X′ is continuous but non-differentiable, then ext(V_X) is also negligible and moreover it is a Cantor set.

It is proved also that these properties are stable under the transformations of the type Y(t)=f(X(t)), if f is a sufficiently smooth function.     

Conditioned Diffusions which are Brownian Bridges

Let X _t be a one-dimensional diffusion of the form dX _t=dB _t+(X _t)dt. Let Tbe a fixed positive number and let be the diffusion process which is X _t conditioned so that X ₀=X _T=x. If the drift is constant, i.e., , then the conditioned diffusion process is a Brownian bridge. In this paper, we show the converse is false. There is a two parameter family of nonlinear drifts with this property.     

A Nonparametric Estimation Problem From Indirect Observations

This paper deals with a nonparametric estimation problem of an integral-type functional from indirect observations where the observation Y (t) is a sum of a known function of an unobservable process X (t) and a Gaussian white noise, and X (t) is a sum of an unknown function a(t) and a Gaussian process. The minimax lower bound on the quality of nonparametric estimation is derived and an asymptotically efficient estimator is proposed. The paper concludes with some examples including one about reduction to parameter estimation.     

Asymptotic properties of solutions of multidimensional stochastic differential equations

Summary LetX _t R ^d be the solution of the stochastic equationdX _t =b(X _t )dt+(X _t )dW _t , whereW _t denotes a standard Wiener process. The aim of the paper is to clarify under which conditions the drift term or the diffusion term is of negligible significance for the long term behaviour ofX _t .     

Evolution equations governed by the sweeping process

This paper is concerned with variants of the sweeping process introduced by J.J. Moreau in 1971. In Section 4, perturbations of the sweeping process are studied. The equation has the formX(t) -N _C(t) (X(t)) +F(t, X(t)). The dimension is finite andF is a bounded closed convex valued multifunction. WhenC(t) is the complementary of a convex set,F is globally measurable andF(t, ·) is upper semicontinuous, existence is proved (Th. 4.1). The Lipschitz constants of the solutions receive particular attention. This point is also examined for the perturbed version of the classical convex sweeping process in Th. 4.1. In Sections 5 and 6, a second-order sweeping process is considered:X (t) -N _C(X(t)) (X(t)). HereC is a bounded Lipschitzean closed convex valued multifunction defined on an open subset of a Hilbert space. Existence is proved whenC is dissipative (Th. 5.1) or when allC(x) are contained in a compact setK (Th. 5.2). In Section 6, the second-order sweeping process is solved in finite dimension whenC is continuous.     

Identifying nonlinear covariate effects in semimartingale regression models

Summary LetX _t be a semimartingale which is either continuous or of counting process type and which satisfies the stochastic differential equationdX _t=Y_t(t, Z_t) dt+dM_t, whereY andZ are predictable covariate processes,M is a martingale and is an unknown, nonrandom function. We study inference for by introducing an estimator for and deriving a functional central limit theorem for the estimator. The asymptotic distribution turns out to be given by a Gaussian random field that admits a representation as a stochastic integral with respect to a multiparameter Wiener process. This result is used to develop a test for independence ofX from the covariateZ, a test for time-homogeneity of , and a goodness-of-fit test for the proportional hazards model (t,z)=₁(t)a ₂(z) used in survival analysis.Research supported by the Army Research Office under Grant DAAL03-86-K-0094Research supported by the Air Force Office of Scientific Research under Contract F49620-85-C-0007     

On the asymptotic behaviour of solutions of stochastic differential equations

Summary In this paper we study the asymptotic behaviour of the solution of the stochastic differential equation dX _t=g(X _t)dt+(X _t)dW _t, where and g are positive functions and W _tis a Wiener process. We clarify, under which conditions X _tmay be approximated on {X _t} by means of a deterministic function. Further the question is treated, whether X _tconverges in distribution on {X _t. We deal with the Ito-solution as well as the Stratonovitch-solution and compare both.Partially supported by the SFB 123 Stochastische Mathematische Modelle, Heidelberg     

Semilinear Perturbations of Harmonic Spaces,Liouville Property and a Boundary Value Problem

Let (X,) be a P-harmonic Bauer space and let be a Borel measurable function on X×R satisfying conditions (A) through (D) of Section 2 (e.g., (x,t)=t|t|^–1 where >1). For every Kato family M of potential kernels on X let ^M U(X) denote the set of all real continuous functions on X such that u+K ^M _D (,u)(D) for every open relatively compact subset D of X. We study the existence of a non-trivial function in ^M U(X) which is dominated by a given positive harmonic function on X. If X is a domain of R ^d , is a positive Kato measure on X and L is a second-order differential operator in R ^d , we apply our study to derive a characterization of finite positive measures on the minimal Martin boundary ^M ₁ X for which the boundary value problem Lu=(,u) in X and u= on ^M ₁ X is solvable.     

The p-Variation and an Extension of the Class of Semimartingales

This is an overview of results concerning the class of càdlàg stochastic processes X such that for t0, X(t)=X(0)+M(t)+A(t), where M is a local martingale and A is an adapted, càdlàg stochastic process for some p<2 having almost all sample functions of bounded p-variation.     

Measure of the multiple self-intersection set of a markov process

Let X(t), t ≧ 0, be a Markov process in R^m with homogeneous transition density p(t; x, y). For a closed bounded set B ? R^m, X is said to have a self-intersection of order r ≧ 2 in B if there are distinct points t₁ < … < t_r such that X(t₁) ∈ B and X(t_j) = X(t¹), for j = 2,…, r. The focus of this work is the Hausdorff measure, suitably defined, of the set of such r-tuples. The main result is that under general conditions on p(t; x, y) as well as the specific condition there is a measure function M(t), defined in terms of the integral above, such that the corresponding Hausdorff measure of self-intersection set is positive, with positive probability. The results are applied to Lévy and diffusion processes, and are shown to extend recent results in this area.     

Multivalued Stochastic Differential Equations: Convergence of a Numerical Scheme

In this paper we show the strong mean square convergence of a numerical scheme for a R ^d -multivalued stochastic differential equation: dX _t +A(X _t )dtb(t,X _t )dt+(t,X _t )dW _t and obtain the rate of convergence O(( log(1/)^1/2) when the diffusion coefficient is bounded. By introducing a discrete Skorokhod problem, we establish L ^p -estimates (p2) for the solutions and prove the convergence by using a deterministic result. Numerical experiments for the rate of convergence are presented.     

The Local Bootstrap for Kernel Estimators under General Dependence Conditions

We consider the problem of estimating the distribution of a nonparametric (kernel) estimator of the conditional expectation g(x; ) = E((X _t+1) | Y _t,m = x) of a strictly stationary stochastic process {X _t , t 1}. In this notation (·) is a real-valued Borel function and Y _t,m a segment of lagged values, i.e., Y_t,m=(X_{t-i 1},X_{t-i 2},...,X_{t-i m}), where the integers i _i , satisfy 0 i₁2...m>. We show that under a fairly weak set of conditions on {X _t , t 1}, an appropriately designed and simple bootstrap procedure that correctly imitates the conditional distribution of X _t+1 given the selective past Y _t,m , approximates correctly the distribution of the class of nonparametric estimators considered. The procedure proposed is entirely nonparametric, its main dependence assumption refers to a strongly mixing process with a polynomial decrease of the mixing rate and it is not based on any particular assumptions on the model structure generating the observations.     

Central limit theorem for stochastically continuous processes. Convergence to stable limit

LetX={X(t), t[0, 1]} be a stochastically continuous cadlag process. Assume that thek dimensional finite joint distributions ofX are in the domain of normal attraction of strictlyp-stable, 0<p<2, measure onR ^k for all 1k<. For functionsf, g such that _p (|X(x–X(u)|) >g(u–s) and _p (|X(s–X(t|)|X(t)–X(u|)>f(u–s), 0 s t u 1, conditions are found which imply that the distributions –(n ^–1/p (X ₁+···+X _n )),n1, converge weakly inD[0, 1] to the distribution of ap-stable process. HereX ₁,X ₂, ... are independent copies ofX and _p (Z)=sup _t<0 t ^pP{|Z|<t} denotes the weakpth moment of a random variable Z.     

Time-Space Harmonic Polynomials for Continuous-Time Processes and an Extension

A time-space harmonic polynomial for a stochastic process M=(M _t) is a polynomial P in two variables such that P(t, M _t) is a martingale. In this paper, we investigate conditions for the existence of such polynomials of each degree in the second, space, argument. We also describe various properties a sequence of time-space harmonic polynomials may possess and the interaction of these properties with distributional properties of the underlying process. Thus, continuous-time conterparts to the results of Goswami and Sengupta,⁽²⁾ where the analoguous problem in discrete time was considered, are derived. A few additional properties are also considered. The resulting properties of the process include independent increments, stationary independent increments and semi-stability. Finally, a generalization to a measure proposed by Hochberg⁽³⁾ on path space is obtained.     

Large Deviations of Local Times of Lévy Processes

For X(t) a real-valued symmetric Lévy process, its characteristic function is E(e ^iX(t))=exp(–t()). Assume that is regularly varying at infinity with index 1<2. Let L ^x _t denote the local time of X(t) and L* _t =sup _xR L ^x _t . Estimates are obtained for P(L ⁰ _t y) and P(L* _t y) as y and t fixed.     

Time-optimal controls of the equation of evolution in a separable and reflexive Banach space

Let a variable, closed, bounded, and convex subset ofX, a separable and reflexive Banach space, be denoted byG(t). Suppose thatG(t) varies upper-semicontinuously with respect to inclusion ast varies in [0,T]. We say that the strongly measurable mapu from [0,T] toX is an admissible control if, for almost everyt in [0,T],u(t) is an element ofU, a closed, bounded, and convex subset ofX, and u _p M ^1p, where p>1 andM>0.Ifx ^u is the weak solution todx/dt+A(t)x=u(t), 0tT, whereA(t) is as defined by Tanabe in Ref. 1, we say that the responsex ^u to the controlu hits the target in timeT ^u ifx ^u (0)=0 andx ^u (T ^u ) is an element ofG(T ^u ). If there is a control with this property, then there is a time-optimal control.     

Perturbations of Generalized Mehler Semigroups and Applications to Stochastic Heat Equations with Levy Noise and Singular Drift

In this paper we solve the Kolmogorov equation and, as a consequence, the martingale problem corresponding to a stochastic differential equation of type dX _t =AX _t dt+b(X _t )dt+dY _t , on a Hilbert space E, where (Y _t ) _t0 is a Levy process on E,A generates a C ₀-semigroup on E and b:EE. Our main point is to allow unbounded A and also singular (in particular, non-continuous) b. Our approach is based on perturbation theory of C ₀-semigroups, which we apply to generalized Mehler semigroups considered on L ²(), where is their respective invariant measure. We apply our results, in particular, to stochastic heat equations with Levy noise and singular drift.     

Asymptotic normality and efficiency of a weighted correlogram

For a process X(t)=Σ _j=1 ^M g _j (t)ξ _j (), where g_j(t) are nonrandom given functions, is a stationary vector-valued Gaussian process, Eξ_k(t) = 0, and Eξ_k(0) Eξ_l(τ) = r _kl(τ), we construct an estimate for the functions r _kl(τ) on the basis of observations X(t), t ∈ [0, T]. We establish conditions for the asymptotic normality of as T → ∞. We consider the problem of the optimal choice of parameters of the estimate depending on observations. Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 50, No. 7, pp. 937–947, July, 1998.     

Intrinsically homogeneous sets,splitting times,and the big shift

Summary Let X be a strong Markov process. Let M be an optional set with the property that _1MoT (S)=1 _M (s+T) whenever s>0 and T is an optional time with [T]M. If L=sup{t>0 tM}M, we show that L is a splitting time of X: the pre-L events and the post-L events are conditionally independent given X _L . To prove this, we extend work of Sharpe's to show that the big shift operators _T and commute with optional projection and dual optional projection, respectively, whenever T is an optional time. Examples are given which are not contained within previous work of Millar and Getoor.