Donsker's theorem

In probability theory, Donsker's theorem (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. In probability theory, Donsker's theorem (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. Let X 1 , X 2 , X 3 , … {displaystyle 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. Let S n := ∑ i = 1 n X i {displaystyle S_{n}:=sum _{i=1}^{n}X_{i}} . The stochastic process S := ( S n ) n ∈ N {displaystyle S:=(S_{n})_{nin mathbb {N} }} is known as a random walk. Define the diffusively rescaled random walk (partial-sum process) by The central limit theorem asserts that W ( n ) ( 1 ) {displaystyle W^{(n)}(1)} converges in distribution to a standard Gaussian random variable W ( 1 ) {displaystyle W(1)} as n → ∞ {displaystyle n o infty } . Donsker's invariance principle extends this convergence to the whole function W ( n ) := ( W ( n ) ( t ) ) t ∈ [ 0 , 1 ] {displaystyle W^{(n)}:=(W^{(n)}(t))_{tin }} . More precisely, in its modern form, Donsker's invariance principle states that: As random variables taking values in the Skorokhod space D [ 0 , 1 ] {displaystyle {mathcal {D}}} , the random function W ( n ) {displaystyle W^{(n)}} converges in distribution to a standard Brownian motion W := ( W ( t ) ) t ∈ [ 0 , 1 ] {displaystyle W:=(W(t))_{tin }} as n → ∞ . {displaystyle n o infty .} Let Fn be the empirical distribution function of the sequence of i.i.d. random variables X 1 , X 2 , X 3 , … {displaystyle X_{1},X_{2},X_{3},ldots } with distribution function F. Define the centered and scaled version of Fn by 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. Theorem (Donsker, Skorokhod, Kolmogorov) The sequence of Gn(x), as random elements of the Skorokhod space D ( − ∞ , ∞ ) {displaystyle {mathcal {D}}(-infty ,infty )} , converges in distribution to a Gaussian process G with zero mean and covariance given by 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 sup t G n ( t ) {displaystyle scriptstyle sup _{t}G_{n}(t)} and supremum of absolute value, sup t | G n ( t ) | {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. In 1952 Donsker stated and proved (not quite correctly) 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 distributions with respect to uniform convergence in t over the interval .