# Il teorema di Donsker

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

Nella teoria delle probabilità, Il teorema di 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.

Permettere {stile di visualizzazione X_{1},X_{2},X_{3},ldot } be a sequence of independent and identically distributed (i.i.d.) random variables with mean 0 and variance 1. Permettere {stile di visualizzazione S_{n}:=somma _{io=1}^{n}X_{io}} . The stochastic process {stile di visualizzazione S:=(S_{n})_{nin mathbb {N} }} is known as a random walk. Define the diffusively rescaled random walk (partial-sum process) di {stile di visualizzazione W ^{(n)}(t):={frac {S_{lfloor ntrfloor }}{mq {n}}},qquad tin [0,1].} The central limit theorem asserts that {stile di visualizzazione W ^{(n)}(1)} converges in distribution to a standard Gaussian random variable {stile di visualizzazione W.(1)} come {displaystyle nto infty } . Donsker's invariance principle[1][2] extends this convergence to the whole function {stile di visualizzazione W ^{(n)}:=(W^{(n)}(t))_{lattina [0,1]}} . Più precisamente, in its modern form, Donsker's invariance principle states that: As random variables taking values in the Skorokhod space {stile di visualizzazione {matematico {D}}[0,1]} , the random function {stile di visualizzazione W ^{(n)}} converges in distribution to a standard Brownian motion {stile di visualizzazione W.:=(w(t))_{lattina [0,1]}} come {displaystyle nto infty .} History Let Fn be the empirical distribution function of the sequence of i.i.d. random variables {stile di visualizzazione X_{1},X_{2},X_{3},ldot } with distribution function F. Define the centered and scaled version of Fn by {stile di visualizzazione G_{n}(X)={mq {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 (normal) random variable G(x) with zero mean and variance F(x)(1 -F(X)) as the sample size n grows.

Teorema (Donsker, Skorochod, Kolmogorov) The sequence of Gn(X), as random elements of the Skorokhod space {stile di visualizzazione {matematico {D}}(-infty ,infty )} , converges in distribution to a Gaussian process G with zero mean and covariance given by {nome dell'operatore dello stile di visualizzazione {cov} [G(S),G(t)]= E[G(S)G(t)]=min{F(S),F(t)}-F(S)} {stile di visualizzazione {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. In 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] In 1952 Donsker stated and proved (not quite correctly)[4] a general extension for the Doob–Kolmogorov heuristic approach. In 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 {stile di visualizzazione {matematico {D}}[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, tale che {stile di visualizzazione |G_{n}-B_{n}|_{infty }} 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). ">

Se vuoi conoscere altri articoli simili a Il teorema di Donsker puoi visitare la categoria Empirical process.

Vai su

Utilizziamo cookie propri e di terze parti per migliorare l'esperienza dell'utente Maggiori informazioni