The Poisson kernel for certain degenerate elliptic operators

Let $\text{L =}\text{1}\text{2}\text{∑}{}_{\mathrm{k\; =\; 1}}{}^{\mathrm{d}}\text{V}{}_{\mathrm{k}}{}^2\text{+ V}{}_0$ be a smooth second order differential operator on $\text{R}$ⁿ written in Hörmander form, and $\text{G}$ be a bounded open set with smooth noncharacteristic boundary. Under a global condition that ensures that the Dirichlet problem is well posed for L on $\text{G}$ and a nondegeneracy condition at the boundary (precisely: the Lie algebra generated by the vector fields V₀, V₁,…, V_d is of full rank on the boundary) then the harmonic measure for L starting at any point in $\text{G}$ has a smooth density with respect to the natural boundary measure. Estimates on the derivatives of this density (the Poisson kernel) similar to the classical estimates for the Poisson kernel for the Laplacian on a half space are given.     

Barriers for uniformly elliptic equations and the exterior cone condition

We consider the mixed boundary value problem $\text{Au = f}\text{in}\text{Ω, B}{}_0\text{u = g}{}_0\text{in}\text{Γ}{}^{\mathrm{?}}\text{, B}{}_1\text{u = g}{}_1\text{in}\text{Γ}{}^{\mathrm{+}}$, where Ω is a bounded open subset of $\text{R}$ⁿ whose boundary Γ is divided into disjoint open subsets Γ⁺ and Γ^? by an (n ? 2)-dimensional manifold ω in Γ. We assume A is a properly elliptic second order partial differential operator on $\text{Ω}$ and B_j, for j = 0, 1, is a normal jth order boundary operator satisfying the complementing condition with respect to A on $\text{Γ}{}^{\mathrm{+}}$. The coefficients of the operators and Γ⁺, Γ^? and ω are all assumed arbitrarily smooth. As announced in [Bull. Amer. Math. Soc.83 (1977), 391–393] we obtain necessary and sufficient conditions in terms of the coefficients of the operators for the mixed boundary value problem to be well posed in Sobolev spaces. In fact, we construct an open subset $\text{T}$ of the reals such that, if $\text{D}{}^{\mathrm{s}}\text{= {u ? H}{}^{\mathrm{s}}\text{(Ω): Au = 0}}$ then for $\text{s ? =}\text{1}\text{2}\text{(}\text{mod}\text{1), (B}{}_0\text{,B}{}_1\text{): D}{}^{\mathrm{s}}\text{→ H}{}^{\mathrm{<mtext>s\; ?\; </mtext><mtext>1</mtext><mtext>2</mtext>}}\text{(Γ}{}^{\mathrm{?}}\text{) × H}{}^{\mathrm{<mtext>s\; ?\; </mtext><mtext>3</mtext><mtext>2</mtext>}}\text{(Γ}{}^{\mathrm{+}}\text{)}$ is a Fredholm operator if and only if s ∈$\text{T}$ . Moreover, $\text{T}$ = ?_xew$\text{T}$_x, where the sets $\text{T}$_x are determined algebraically by the coefficients of the operators at x. If n = 2, $\text{T}$_x is the set of all reals not congruent (modulo 1) to some exceptional value; if n = 3, $\text{T}$_x is either an open interval of length 1 or is empty; and finally, if n ? 4, $\text{T}$_x is an open interval of length 1.     

Generic hyperbolicity for the equilibria of the one-dimensional parabolic equation ut=(a(x)ux)x+f(u)

We show, for some classes of diffusion coefficients that, generically in f, all equilibria of the reaction–diffusion equation$\text{u}{}_{\mathrm{t}}\text{=(a(x)u}{}_{\mathrm{x}}\text{)}{}_{\mathrm{x}}\text{+f(u),}\text{0<x<1}$with homogeneous Neumann boundary conditions are hyperbolic.     

Sufficient conditions for ergodicity and recurrence of Markov chains on a general state space

Let {X_n} be a ?-irreducible Markov chain on an arbitrary space. Sufficient conditions are given under which the chain is ergodic or recurrent. These extend known results for chains on a countable state space. In particular, it is shown that if the space is a normed topological space, then under some continuity conditions on the transition probabilities of {X_n} the conditions for ergodicity will be met if there is a compact set K and an ? > 0 such that $\text{E}\text{{6X}{}_{\mathrm{n+1}}\text{6 — 6X}{}_{\mathrm{n}}\text{6 ∣ X}{}_{\mathrm{n}}\text{= x} ? ??}$ whenever x lies outside K and $\text{E}\text{{6X}{}_{\mathrm{n+1}}\text{6 ∣ X}{}_{\mathrm{n}}\text{=x}}$ is bounded, x ∈ K; whilst the conditions for recurrence will be met if there exists a compact K with $\text{E}\text{{6X}{}_{\mathrm{n+1}}\text{6 ? 6X}{}_{\mathrm{n}}\text{6 ∣ X}{}_{\mathrm{n}}\text{= x} ? 0}$ for all x outside K. An application to queueing theory is given.     

Hilbert space representations of general discrete time stochastic processes

We show that under mild conditions the joint densities Px₁,…,x_n) of the general discrete time stochastic process X_n on $\text{pH}$ can be computed via

$\text{Px}{}_1\text{,…,x}{}_{\mathrm{n}}\text{(x}{}_1\text{,…,x}{}_{\mathrm{n}}\text{) = 6?T(x}{}_1\text{)…T(x}{}_{\mathrm{n}}\text{)6}{}^2$

where ? is in a Hilbert space $\text{pH}$, and T (x), x ? $\text{pH}$ are linear operators on $\text{pH}$. We then show how the Central Limit Theorem can easily be derived from such representations.     

On fair coin-tossing games

Let Ω be a finite set with k elements and for each integer $\text{n ≧ 1}$ let $\text{Ω}{}_{\mathrm{n}}\text{= Ω × Ω × … × Ω}$ (n-tuple) and $\text{Ω}{}_{\mathrm{n}}\text{= {(a}{}_1\text{, a}{}_2\text{,…, a}{}_{\mathrm{n}}\text{) | (a}{}_1\text{, a}{}_2\text{,…, a}{}_{\mathrm{n}}\text{) ∈ Ω}{}_{\mathrm{n}}$ and a_j ≠ a_j+1 for some 1 ≦ j ≦ n ? 1}. Let {Y_m} be a sequence of independent and identically distributed random variables such that P(Y₁ = a) = k^?1 for all a in Ω. In this paper, we obtain some very surprising and interesting results about the first occurrence of elements in $\text{Ω}{}_{\mathrm{n}}$ and in Ω?_n with respect to the stochastic process {Y_m}. The results here provide us with a better and deeper understanding of the fair coin-tossing (k-sided) process.     

Singular perturbation problems for systems of partial differential equations of elliptic type

Elliptic boundary value problems for systems of nonlinear partial differential equations of the form $\text{F}{}_{\mathrm{i}}\text{(x, u}{}_1\text{, u}{}_2\text{,…, u}{}_{\mathrm{N}}\text{,}\text{?u}{}_{\mathrm{i}}\text{?x}{}_{\mathrm{j}}\text{, ?}{}^{\mathrm{p}}{}_{\mathrm{i}}\text{?}{}^2\text{u}{}_{\mathrm{i}}\text{?x}{}_{\mathrm{j}}\text{?x}{}_{\mathrm{k}}\text{) = ?}{}_{\mathrm{i}}\text{(x), x ? R}{}^{\mathrm{n}}$, i = 1(1)N, j, k = 1(1)n, p_i ? 0, ? being a small parameter, with Dirichlet boundary conditions are considered. It is supposed that a formal approximation Z is given which satisfies the boundary conditions and the differential equations upto the order χ(?) = o(1) in some norm. Then, using the theory of differential inequalities, it is shown that under certain conditions the difference between the exact solution u of the boundary value problem and the formal approximation Z, taken in the sense of a suitable norm, can be made small.     

On the simulation of shot noise and some other random variables

It is shown that the random voltage V_t resulting from pulses with independent random amplitude Y_i Poisson arrivals, and exponential decay, can be asymptotically represented, in the stationary case, by the following random variable; namely a sum of products of random variables:

$\text{W=}\text{∑}\text{i=1}\text{∞}\text{U}{}_{\mathrm{i}}\text{Y}{}_{\mathrm{i}}\text{,}$

where

$\text{U}{}_{\mathrm{i}}\text{=}\text{∏}\text{j=1}\text{i}\text{X}{}_{\mathrm{j}}{}^{\mathrm{\beta /\lambda }}\text{.}$

Here X_j are independent uniform random variables, β>0 is the decay parameter, λ>0 is the rate of the Poisson process.     

On the law of large numbers for stationary sequences

We show that if X_i is a stationary sequence for which $\text{S}{}_{\mathrm{n}}\text{B}{}_{\mathrm{n}}$ converges to a finite non zero random variable of constant sign, where S_n=X₁+X₂+?+X_n and B_n is a sequence of constants, then B_n is regularly varying with index 1. If in addition $\text{Σ}\text{P}\text{(|X}{}_1\text{|>B}{}_{\mathrm{n}}$ is finite, then $\text{E}\text{|X}{}_1\text{|}$ is finite, and if in addition to this X_i satisfies an asymptotic independence condition, $\text{E}\text{X}{}_1\text{≠ 0}$.     

Singular perturbation problems for linear parabolic differential operators of arbitrary order

We consider the first initial-boundary value problem for $\text{(}\text{?u}\text{?t}\text{) + ?L}{}_1\text{u + L}{}_0\text{u = f(L}{}_0$ and L₁ are linear elliptic partial differential operators) and investigate the properties of u(x, t, ?) as ? ↓ 0 in the maximum norm. Special attention is paid to approximations obtained by the boundary layer method. We use a priori estimates.     

Some comments on the hazard gradient

For random variables T₁,…,T_n, the gradient of $\text{R(t) = ?log}\text{P}\text{{T}{}_1\text{> t}{}_1\text{,…,T}{}_{\mathrm{n}}\text{> t}{}_{\mathrm{n}}\text{}}$ is called the hazard gradient. Some properties of this multivariate version of the hazard rate are demonstrated, and some examples are given to show the usefulness of the hazard gradient in characterizing distributions or families of distributions.     

Algebras containing a divisible unimodular sequence with dense self adjoint span

We characterize the uniform algebras A on a compact Hausdorff space X which contain a sequence {u_j}_{j = 0}^∞ of unimodular elements with $\text{u}{}_{\mathrm{j}}\text{u}{}_{\mathrm{j\; ?\; 1}}\text{? A}$ and closed span $\text{{u}{}_{\mathrm{j}}\text{u}\text{}}{}_{\mathrm{j\; =\; 0}}{}^{\mathrm{\infty }}\text{= C(X)}$ in terms of the maximal ideal space of A. Roughly, the essential set of A looks like (at most) countably many copies of the boundary of the unit disk, and A looks like the disk algebra on each.     

On Berry-Esséen rates,a law of the iterated logarithm and an invariance principle for the proportion of the sample below the sample mean

Let F_n(x) be the empirical distribution function based on n independent random variables X₁,…,X_n from a common distribution function F(x), and let $\text{X}\text{= Σ}{}_{\mathrm{i=1}}{}^{\mathrm{n}}\text{X}{}_{\mathrm{i}}\text{n}$ be the sample mean. We derive the rate of convergence of $\text{F}{}_{\mathrm{n}}\text{(}\text{X}\text{)}$ to normality (for the regular as well as nonregular cases), a law of iterated logarithm, and an invariance principle for $\text{F}{}_{\mathrm{n}}\text{(}\text{X}\text{)}$.     

Delayed random walks

A delayed random walk $\text{{S}{}^1{}_{\mathrm{n}}\text{, n ≥ 0}}$ is defined here as a partial sum process of independent random variables in which the first N summands (N optional) are distributed F¹,…,F_N, respectively, while all remaining summands are distributed F₀, where {F_k, _k ≥ 0} is a sequence of proper distribution functions on the real line. Delayed random walks arise naturally in the study of certain generalized single server queues. This paper examines optional times of the process such as $\text{π = inf {n: n ≥ 1 and S}{}^1{}_{\mathrm{n}}\text{≥ 0}}$. Conditions insuring the finiteness of E {π} and E {π²} are obtained, generating functions calculated, and illustrative examples given. The bivariate functions $\text{E{r}{}^{\mathrm{\pi }}\text{explsqbitS}{}^1{}_{\mathrm{\pi }}\text{rsqb}}$ and $\text{E {}\text{∑}\text{n=0}\text{π?1}\text{explsqbitS}{}^1{}_{\mathrm{n}}\text{rsqb}}$ are studied for the case where N ≡ 1.     

Normalité asymptotique des estimateurs semiparamétriques dans le modèle de Cox avec covariable manquante nonignorable

Let T denote a random duration until some event of interest. In the Cox model $\text{λ}{}_{\mathrm{T}}\text{(t)}\text{e}{}^{\mathrm{\beta Z(t)}}$, if the value of Z at event time is unobserved, Dupuy and Mesbah (Lifetime Data Analysis 8 (2002) 99–115) have proposed to estimate the parameters β and $\text{Λ}{}_{\mathrm{T}}\text{(t)=∫}{}_0{}^{\mathrm{t}}\text{λ}{}_{\mathrm{T}}\text{(s)}\text{d}\text{s}$ by maximizing a likelihood obtained from a joint model for survival and the longitudinal covariate data. We show that the estimators derived from this joint likelihood are asymptotically normally distributed. To cite this article: J.-F. Dupuy et al., C. R. Acad. Sci. Paris, Ser. I 336 (2003).     

Bounce law at the corners of convex billiards

Let C be a convex subset of $\text{R}{}^{\mathrm{n}}$. Given any elastic shock solution x(·) of the differential inclusion$\text{x}\text{̈}\text{(t)+N}{}_{\mathrm{C}}\text{(x(t))∋0,}\text{t>0,}$the bounce of the trajectory at a regular point of the boundary of C follows the Descartes law. The aim of the paper is to exhibit the bounce law at the corners of the boundary. For that purpose, we define a sequence (C_ε) of regular sets tending to C as ε→0, then we consider the approximate differential inclusion , and finally we pass to the limit when ε→0. For approximate sets defined by $\text{C}{}_{\mathrm{\epsilon }}\text{=C+ε}\text{B}$ (where $\text{B}$ is the unit euclidean ball of $\text{R}{}^{\mathrm{n}}$), we recover the bounce law associated with the Moreau–Yosida regularization.     

On Guerra's broken replica-symmetry bound

Consider a random Hamiltonian $\text{H}{}_{\mathrm{N}}\text{(}\text{σ}\text{→}\text{)}$ for $\text{σ}\text{→}\text{∈Σ}{}_{\mathrm{N}}\text{={0,1}}{}^{\mathrm{<mtext>N</mtext>}}\text{.}$ We assume that the family $\text{(H}{}_{\mathrm{N}}\text{(}\text{σ}\text{→}\text{))}$ is jointly Gaussian centered and that for $\text{σ}\text{→}{}^1\text{,}\text{σ}\text{→}{}^2\text{∈Σ}{}_{\mathrm{N}}\text{,}$$\text{N}{}^{\mathrm{?1}}\text{EH}{}_{\mathrm{N}}\text{(}\text{σ}\text{→}{}^1\text{)H}{}_{\mathrm{N}}\text{(}\text{σ}\text{→}{}^2\text{)}$ =ξ(N^?1∑_i?Nσ¹_iσ²_i) for a certain function ξ on $\text{R}$. F. Guerra proved the remarkable fact that the free energy of the system with Hamiltonian $\text{H}{}_{\mathrm{N}}\text{(}\text{σ}\text{→}\text{)+h∑}{}_{\mathrm{i?N}}\text{σ}{}_{\mathrm{i}}$ is bounded below by the free energy of the Parisi solution provided that ξ is convex on $\text{R}$. We prove that this fact remains (asymptotically) true when the function ξ is only assumed to be convex on $\text{R}{}^{\mathrm{+}}$. This covers in particular the case of the p-spin interaction model for any p. To cite this article: M. Talagrand, C. R. Acad. Sci. Paris, Ser. I 337 (2003).     

Wr,p(R)-splines

In [3] Golomb describes, for 1 < p < ∞, the H^r,p(R)-extremal extension $\text{F}{}_1$ of a function $\text{?:E → R}$ (i.e., the H^r,p-spline with knots in E) and studies the cone $\text{H}{}_{1E}{}^{\mathrm{r,p}}$ of all such splines. We study the problem of determining when $\text{F}{}_1$ is in W^r,p ≡ H^r,p ∩ L^p. If $\text{F}{}_1\text{? W}{}^{\mathrm{r,p}}$, then $\text{F}{}_1$ is called a W^r,p-spline, and we denote by $\text{W}{}_{1E}{}^{\mathrm{r,p}}$ the cone of all such splines. If E is quasiuniform, then $\text{F}{}_1\text{? W}{}^{\mathrm{r,p}}$ if and only if . The cone $\text{W}{}_{1E}{}^{\mathrm{r,p}}$ with E quasiuniform is shown to be homeomorphic to l^p. Similarly, $\text{H}{}_{1E}{}^{\mathrm{r,p}}$ is homeomorphic to h^r,p. Approximation properties of the W^r,p-splines are studied and error bounds in terms of the mesh size $\text{¦ E ¦}$ are calculated. Restricting ourselves to the case p = 2 and to quasiuniform partitions E, the second integral relation is proved and better error bounds in terms of $\text{¦ E ¦}$ are derived.     

On the fair coin tossing process

Let Ω = {1, 0} and for each integer n ≥ 1 let $\text{Ω}{}_{\mathrm{n}}\text{= Ω × Ω × … × Ω}$ (n-tuple) and $\text{Ω}{}_{\mathrm{n}}{}^{\mathrm{k}}\text{= {(a}{}_1\text{, a}{}_2\text{, …, a}{}_{\mathrm{n}}\text{)|(a}{}_1\text{, a}{}_2\text{, … , a}{}_{\mathrm{n}}\text{) ? Ω}{}_{\mathrm{n}}\text{and}\text{Σ}{}_{\mathrm{i=1}}{}^{\mathrm{n}}\text{a}{}_{\mathrm{i}}\text{= k}}$ for all k = 0,1,…,n. Let {Y_m}_m≥1 be a sequence of i.i.d. random variables such that $\text{P(Y}{}_1\text{= 0) = P(Y}{}_1\text{= 1) =}\text{1}\text{2}$. For each A in $\text{Ω}{}_{\mathrm{n}}$, let T_A be the first occurrence time of A with respect to the stochastic process {Y_m}_m≥1. R. Chen and A.Zame (1979, J. Multivariate Anal. 9, 150–157) prove that if n ≥ 3, then for each element A in $\text{Ω}{}_{\mathrm{n}}$, there is an element B in $\text{Ω}{}_{\mathrm{n}}$ such that the probability that T_B is less than T_A is greater than $\text{1}\text{2}$. This result is sharpened as follows: (I) for n ≥ 4 and 1 ≤ k ≤ n ? 1, each element A in $\text{Ω}{}_{\mathrm{n}}{}^{\mathrm{k}}$, there is an element B also in $\text{Ω}{}_{\mathrm{n}}{}^{\mathrm{k}}$ such that the probability that T_B is less than T_A is greater than $\text{1}\text{2}$; (II) for n ≥ 4 and 1 ≤ k ≤ n ? 1, each element A = (a₁, a₂,…,a_n) in $\text{Ω}{}_{\mathrm{n}}{}^{\mathrm{k}}$, there is an element C also in $\text{Ω}{}_{\mathrm{n}}{}^{\mathrm{k}}$ such that the probability that T_A is less than T_C is greater than $\text{1}\text{2}$ if n ≠ 2m or n = 2m but a_i = a_{i + 1} for some 1 ≤ i ≤ n?1. These new results provide us with a better and deeper understanding of the fair coin tossing process.