A note on the normal approximation error for randomly weighted self-normalized sums

Let X = {X _n } _n≥1 and Y = {Y _n } _n≥1 be two independent random sequences. We obtain rates of convergence to the normal law of randomly weighted self-normalized sums $$\psi _n \left( {X,Y} \right) = \sum\nolimits_{i = 1}^n {{{X_i Y_i } \mathord{\left/ {\vphantom {{X_i Y_i } {V_n , V_n }}} \right. \kern-\nulldelimiterspace} {V_n , V_n }}} = \sqrt {Y_1^2 + \cdots + Y_n^2 } .$$ . These rates are seen to hold for the convergence of a number of important statistics, such as for instance Student’s t-statistic or the empirical correlation coefficient.     

Limit theorems for a class of diffusion processes

We consider a diffusion process {x(t)} on a compact Riemannian manifold with generator δ/2 + b. A current‐valued continuous stochastic process {X _t} in the sense of Itô [8] corresponds to {x(t)} by considering the stochastic line integral X _t(a) along {x(t)} for every smooth 1-form a. Furthermore {X _t} is decomposed into the martingale part and the bounded variation part as a current-valued continuous process. We show the central limit theorems for {X _t} and the martingale part of {X _t}. Occupation time laws for recurrent diffusions and homogenization problems of periodic diffusions are closely related to these theorems     

The distribution of the likelihood ratio for additive processes

Let {X(t), 0 ≤ t ≤ T} and {Y(t), 0 ≤ t ≤ T} be two additive processes over the interval [0, T] which, as measures over D[0, T], are absolutely continuous with respect to each other. Let μ_X and μ_Y be the measures over D[0, T] determined by the two processes. The characteristic function of $\text{ln}\text{(}\text{dμ}{}_{\mathrm{X}}\text{dμ}{}_{\mathrm{Y}}\text{)}$ with respect to μ_Y is obtained in terms of the determining parameters of the two processes.     

Stochastic integral representation of some martingales

Let {X_t} be a continuous square integrable martingale. Denote its increasing (natural) process by {A_t}. Let S_t, T_t be the left and right inverses of A_t, respectively. Then for any square integrable martingale {Y_t} defined on {X_t}, Y_t = ∝₀^tψ_sdX_s, R₀ < t < S_∞ where S_∞ = lim_t→∞S_t, R₀ = inf {t: X_t ≠ 0} provided that Y(T(t)) is σ(X(T(s)): s ? t)-measurable. All martingales are assumed to be zero at t = 0. Brownian motion and Poisson processes are considered also.     

Un survol de la theorie de l'integrale stochastique

The objective of this paper is to present the principal results of a large part of stochastic calculus in a manner that should be comprehensible to readers having only the general notions of stochastic processes. Not all the theorems are proved in detail, but all the fundamental theorems are explained with clarity and precision, and with special attention to the motivations behind them.Given two real valued stochastic processes X and Y, the basic problem is to give a meaning to Z = ∫ Y dX in such a way that the integral sign is not misused. If X is a process whose paths are of bounded variation, then Z should coincide with the ordinary Lebesgue-Stieltjes integral taken path by path. If Y is a left continuous step function, then Z should coincide with the obvious choice: if Y is constant on ]t, u], then Z_u-Z_t is that constant times X_u-X_t. And finally, the Lebesgue dominated convergence theorem should hold: if the processes Yⁿ converge to Y and all the Yⁿ are dominated by a process Y' for which ∝ Y' dX is well defined, then Z_n = ∫ Yⁿ dX should converge to Z = ∫ Y dX in some sense.Starting with these requirements, it is shown that, if ∫ Y dX is defined for all predictable Y, then X must be a semimartingale. Conversely, the integral is well defined for all predictable Y and all semimartingales X.With the integrals defined, a number of their important properties are discussed. In particular, the integral Z is a semimartingale, and a change of variable formula (Ito's formula) holds for $\text{?(Z)}$. Finally, stochastic integral equations are introduced, and a general theorem is given on the existence and uniqueness of solutions.A bibliography with commentaries supplements the text for the benefit of those who would like to go deeper into the subject.     

Supervised classification of diffusion paths

Let X = (X _t ) _t∈[0,1] be a stochastic process with label Y ∈ {0, 1}.We assume that X is some Brownian diffusion when Y = 0, while X is another Brownian diffusion when Y = 1. Based on an explicit computation of the Bayes rule, we construct an empirical classification rule $\hat g$ drawn from an i.i.d. sample of copies of (X, Y). In a nonparametric setting, we prove that $\hat g$ is a consistent rule, and we derive its rate of convergence under mild assumptions on the model.     

On the reliability of stochastic systems

In this note we consider the problem of computing the probability R(t₀ = P(X(t) > Y(t) for 0 < t ? t₀), where X(t) and Y(t) are stochastic processes. This extends some of the existing results to the case of stochastic processes. Related estimation problems are also considered.     

Some remarks on spectral representation for symmetric stable processes

For the symmetric α-stable stochastic process X={X_t∶t∈T} with reproducing kernel space H(X) ? L_α constructed in § 1 we define the following parameters: $\alpha _0 = \sup {\mathbf{ }}\{ \beta \in (0.2]:{\mathbf{ }}\mathcal{H}\mathcal{X}$ embeds isometrically into some L^β}, containsl _β ⁿ 's uniformly}. In §2 we show that for α₀ > α the stochastic process X admits the representation $$X_t = \smallint Y_t (w){\mathbf{ }}Z_\alpha (dw),{\mathbf{ }}t \in T,$$ where {Y_t∶t∈T} itself is a symmetric stable process and Z_α is a symmetric α-stable independently scattered random measure. We show also how some properties of the stochastic process {X_t∶t∈T} depend on the corresponding properties of the process {Y_t∶t∈T}.     

Limiting distributions of functionals of Markov chains

Let \s{X_n, n ? 0\s} and \s{Y_n, n ? 0\s} be two stochastic processes such that Y_n depends on X_n in a stationary manner, i.e. P(Y_n ? A\vbX_n) does not depend on n. Sufficient conditions are derived for Y_n to have a limiting distribution. If X_n is a Markov chain with stationary transition probabilities and Y_n = f(X_n,..., X_n+k) then Y_n depends on X_n is a stationary way. Two situations are considered: (i) \s{X_n, n ? 0\s} has a limiting distribution (ii) \s{X_n, n ? 0\s} does not have a limiting distribution and exits every finite set with probability 1. Several examples are considered including that of a non-homogeneous Poisson process with periodic rate function where we obtain the limiting distribution of the interevent times.     

Ruin problems with compounding assets

We consider a generalization of the classical model of collective risk theory. It is assumed that the cumulative income of a firm is given by a process X with stationary independent increments, and that interest is earned continuously on the firm's assets. Then Y(t), the assets of the firm at time t, can be represented by a simple path-wise integral with respect to the income process X. A general characterization is obtained for the probability r(y) that assets will ever fall to zero when the initial asset level is y (the probability of ruin). From this we obtain a general upper bound for r(y), a general solution for the case where X has no negative jumps, and explicit formulas for three particular examples.In addition, an approximation theorem is proved using the weak convergence theory for stochastic processes. This shows that if the income process is well approximated by Brownian motion with drift, then the assets process Y is well approximated by a certain diffusion process Y₁, and r(y) is well approximated by a corresponding first passage probability r₁(y). The diffusion Y₁, which we call compounding Brownian motion, is closely related to the classical Ornstein-Uhlenbeck process.     

Measure-valued diffusion

We consider the class of continuous measure-valued processes {μ _t } on a finite-dimensional Euclidean space X for which ∫fd μ _t is a semimartingale with absolutely continuous characteristics with respect to t for all f:X→R smooth enough. It is shown that, under some general condition, the Markov process with this property can be obtained as a weak limit for systems of randomly interacting particles that are moving in X along the trajectories of a diffusion process in X as the number of particles increases to infinity.     

Self-similar processes with independent increments

Summary A stochastic process {X _t t 0} onR ^d is called wide-sense self-similar if, for eachc>0, there are a positive numbera and a functionb(t) such that {X _ct } and {aX _t +b(t)} have common finite-dimensional distributions. If {X _t } is widesense self-similar with independent increments, stochastically continuous, andX ₀=const, then, for everyt, the distribution ofX _t is of classL. Conversely, if is a distribution of classL, then, for everyH>0, there is a unique process {X _(H) ^t } selfsimilar with exponentH with independent increments such thatX ₁ has distribution . Consequences of this characterization are discussed. The properties (finitedimensional distributions, behaviors for small time, etc.) of the process {X _(H) ^t } (called the process of classL with exponentH induced by ) are compared with those of the Lévy process {Y _t } such thatY ₁ has distribution . Results are generalized to operator-self-similar processes and distributions of classOL. A process {X _t } onR ^d is called wide-sense operator-self-similar if, for eachc>0, there are a linear operatorA _c and a functionb _c (t) such that {X _ct } and {A _c X _t +b _c (t)} have common finite-dimensional distributions. It is proved that, if {X _t } is wide-sense operator-self-similar and stochastically continuous, then theA _c can be chosen asA _c =c ^Q with a linear operatorQ with some special spectral properties. This is an extension of a theorem of Hudson and Mason [4].     

Weak convergence with random indices

Suppose {X_nn?-0} are random variables such that for normalizing constants a_n>0, b_n, n?0 we have Y_n(·)=(X[_{n, ·}]-b_n/a_n ? Y(·) in D(0.∞) . Then a_n and b_n must in specific ways and the process Y possesses a scaling property. If {N_n} are positive integer valued random variables we discuss when Y_Nn → Y and Y'_n=(X[_Nn]-b_n)/a_n ? Y'. Results given subsume random index limit theorems for convergence to Brownian motion, stable processes and extremal processes.     

A branching particle system approximation for nonlinear stochastic filtering

The optimal filter π = {π t,t ∈ [0,T ]} of a stochastic signal is approximated by a sequence {π n t } of measure-valued processes defined by branching particle systems in a random environment(given by the observation process).The location and weight of each particle are governed by stochastic differential equations driven by the observation process,which is common for all particles,as well as by an individual Brownian motion,which applies to this specific particle only.The branching mechanism of each particle depends on the observation process and the path of this particle itself during its short lifetime δ = n 2α,where n is the number of initial particles and α is a fixed parameter to be optimized.As n →∞,we prove the convergence of π n t to π t uniformly for t ∈ [0,T ].Compared with the available results in the literature,the main contribution of this article is that the approximation is free of any stochastic integral which makes the numerical implementation readily available.     

Shy couplings

A pair (X, Y) of Markov processes on a metric space is called a Markov coupling if X and Y have the same transition probabilities and (X, Y) is a Markov process. We say that a coupling is “shy” if inf _{t ≥ 0} dist(X _t , Y _t ) >  0 with positive probability. We investigate whether shy couplings exist for several classes of Markov processes.     

Sur la convergence uniforme presque complète dans l'estimation de la densité spectrale d'un processus à temps continu après échantillonnage du temps

Let $\text{X={X(t)}}{}_{\mathrm{<mtext>t\in </mtext><mtext>R</mtext>}}$ be a continuous-time strictly stationary and strongly mixing process. In this paper, we prove in the setting of spectral density estimation, at first, under some hard conditions on the spectral density φ_X (because of aliasing phenomenon), the uniformly complete convergence of the spectral density estimate from periodic sampling. Afterwards, to overcome aliasing, we consider the sampled process $\text{{X(t}{}_{\mathrm{n}}\text{)}}{}_{\mathrm{<mtext>n\in </mtext><mtext>Z</mtext>}}$, where {t_n} is a stationary point process independent from X. The uniform complete convergence of the spectral estimate based on the discrete time observations {X(t_k),t_k} is also obtained. The convergence rates are also established. To cite this article: M. Rachdi, C. R. Acad. Sci. Paris, Ser. I 337 (2003).     

A duality relation for entrance and exit laws for Markov processes

Markov processes X_t on (X, F_X) and Y_t on (Y, F_Y) are said to be dual with respect to the function f(x, y) if E_xf(X_t, y) = E_yf(x, Y_t for all x ? X, y ? Y, t ? 0. It is shown that this duality reverses the role of entrance and exit laws for the processes, and that two previously published results of the authors are dual in precisely this sense. The duality relation for the function f(x, y) = 1_{x<y} is established for one-dimensional diffusions, and several new results on entrance and exit laws for diffusions, birth-death processes, and discrete time birth-death chains are obtained.     

Mann-Whitney test for associated sequences

Let {X ₁, ...,X _m } and {Y ₁, ...,Y _n } be two samples independent of each other, but the random variables within each sample are stationary associated with one dimensional marginal distribution functionsF andG, respectively. We study the properties of the classical Wilcoxon-Mann-Whitney statistic for testing for stochastic dominance in the above set up.     

Diffusion approximations of age-and-position dependent branching processes

By a (G, F, h) age-and-position dependent branching process we mean a process in which individuals reproduce according to an age dependent branching process with age distribution function G(t) and offspring distribution generating function F, the individuals (located in R^N) can not move and the distance of a new individual from its parent is governed by a probability density function h(r). For each positive integer n, let Z_n(t,dx) be the number of individuals in dx at time t of the (G, F_n,h_n) age-and-position dependent branching process. It is shown that under appropriate conditions on G, F_n and h_n, the finite dimensional distribution of $\text{Z}{}_{\mathrm{n}}\text{(nt, dx)}\text{n}$ converges, as n → ∞, to the corresponding law of a diffusion continuous state branching process X(t,dx) determined by a ψ-semigroup {ψ_t: t ? 0}. The ψ-semigroup {ψ_t} is the solution of a non-linear evolution equation. A semigroup convergence theorem due to Kurtz [10], which gives conditions for convergence in distribution of a sequence of non-Markovian processes to a Markov process, provides the main tools.