Théorème de Donsker

Donsker's theorem Donsker's invariance principle for simple random walk on {style d'affichage mathbb {Z} } .

En théorie des probabilités, Théorème de Donsker (also known as Donsker's invariance principle, or the functional central limit theorem), named after Monroe D. Donsker, is a functional extension of the central limit theorem.

Laisser {style d'affichage X_{1},X_{2},X_{3},ldots } be a sequence of independent and identically distributed (i.i.d.) random variables with mean 0 and variance 1. Laisser {style d'affichage S_{n}:=somme _{je=1}^{n}X_{je}} . The stochastic process {style d'affichage S:=(S_{n})_{nin mathbb {N} }} is known as a random walk. Define the diffusively rescaled random walk (partial-sum process) par {style d'affichage W ^{(n)}(t):={frac {S_{lfloor ntrfloor }}{sqrt {n}}},qquad tin [0,1].} The central limit theorem asserts that {style d'affichage W ^{(n)}(1)} converges in distribution to a standard Gaussian random variable {style d'affichage W.(1)} comme {style d'affichage nto infty } . Donsker's invariance principle[1][2] extends this convergence to the whole function {style d'affichage W ^{(n)}:=(W ^{(n)}(t))_{étain [0,1]}} . Plus précisément, in its modern form, Donsker's invariance principle states that: As random variables taking values in the Skorokhod space {style d'affichage {mathématique {ré}}[0,1]} , the random function {style d'affichage W ^{(n)}} converges in distribution to a standard Brownian motion {style d'affichage W.:=(O(t))_{étain [0,1]}} comme {style d'affichage nto infty .} History Let Fn be the empirical distribution function of the sequence of i.i.d. random variables {style d'affichage X_{1},X_{2},X_{3},ldots } with distribution function F. Define the centered and scaled version of Fn by {style d'affichage G_{n}(X)={sqrt {n}}(F_{n}(X)-F(X))} indeXed by x ∈ R. By the classical central limit theorem, for fixed x, the random variable Gn(x) converges in distribution to a Gaussian (Ordinaire) random variable G(X) with zero mean and variance F(X)(1 − F(X)) as the sample size n grows.

Théorème (Donsker, Skorokhod, Kolmogorov) The sequence of Gn(X), as random elements of the Skorokhod space {style d'affichage {mathématique {ré}}(-infime ,infime )} , converges in distribution to a Gaussian process G with zero mean and covariance given by {nom de l'opérateur de style d'affichage {cov} [g(s),g(t)]=E[g(s)g(t)]=min{F(s),F(t)}-F(s)} {style d'affichage {F}(t).} The process G(X) can be written as B(F(X)) where B is a standard Brownian bridge on the unit interval.

Kolmogorov (1933) showed that when F is continuous, the supremum {displaystyle scriptstyle sup _{t}G_{n}(t)} and supremum of absolute value, {displaystyle scriptstyle sup _{t}|G_{n}(t)|} converges in distribution to the laws of the same functionals of the Brownian bridge B(t), see the Kolmogorov–Smirnov test. Dans 1949 Doob asked whether the convergence in distribution held for more general functionals, thus formulating a problem of weak convergence of random functions in a suitable function space.[3] Dans 1952 Donsker stated and proved (not quite correctly)[4] a general extension for the Doob–Kolmogorov heuristic approach. Dans the original paper, Donsker proved that the convergence in law of Gn to the Brownian bridge holds for Uniform[0,1] distributions with respect to uniform convergence in t over the interval [0,1].[2] However Donsker's formulation was not quite correct because of the problem of measurability of the functionals of discontinuous processes. In 1956 Skorokhod and Kolmogorov defined a separable metric d, called the Skorokhod metric, on the space of càdlàg functions on [0,1], such that convergence for d to a continuous function is equivalent to convergence for the sup norm, and showed that Gn converges in law in {style d'affichage {mathématique {ré}}[0,1]} to the Brownian bridge.

Later Dudley reformulated Donsker's result to avoid the problem of measurability and the need of the Skorokhod metric. One can prove[4] that there exist Xi, iid uniform in [0,1] and a sequence of sample-continuous Brownian bridges Bn, tel que {style d'affichage |G_{n}-B_{n}|_{infime }} is measurable and converges in probability to 0. An improved version of this result, providing more detail on the rate of convergence, is the Komlós–Major–Tusnády approximation.

See also Glivenko–Cantelli theorem Kolmogorov–Smirnov test References ^ Donsker, M.D. (1951). ">

Si vous voulez connaître d'autres articles similaires à Théorème de Donsker vous pouvez visiter la catégorie Processus empirique.