Empirical process

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In probability theory, an empirical process is a stochastic process that characterizes the deviation of the empirical distribution function from its expectation. In mean field theory, limit theorems (as the number of objects becomes large) are considered and generalise the central limit theorem for empirical measures. Applications of the theory of empirical processes arise in non-parametric statistics.[1]


For X1, X2, ... Xn independent and identically-distributed random variables in R with common cumulative distribution function F(x), the empirical distribution function is defined by

where IC is the indicator function of the set C.

For every (fixed) x, Fn(x) is a sequence of random variables which converge to F(x) almost surely by the strong law of large numbers. That is, Fn converges to F pointwise. Glivenko and Cantelli strengthened this result by proving uniform convergence of Fn to F by the Glivenko–Cantelli theorem.[2]

A centered and scaled version of the empirical measure is the signed measure

It induces a map on measurable functions f given by

By the central limit theorem, converges in distribution to a normal random variable N(0, P(A)(1 − P(A))) for fixed measurable set A. Similarly, for a fixed function f, converges in distribution to a normal random variable , provided that and exist.


is called an empirical process indexed by , a collection of measurable subsets of S.
is called an empirical process indexed by , a collection of measurable functions from S to .

A significant result in the area of empirical processes is Donsker's theorem. It has led to a study of Donsker classes: sets of functions with the useful property that empirical processes indexed by these classes converge weakly to a certain Gaussian process. While it can be shown that Donsker classes are Glivenko–Cantelli classes, the converse is not true in general.


As an example, consider empirical distribution functions. For real-valued iid random variables X1, X2, ..., Xn they are given by

In this case, empirical processes are indexed by a class It has been shown that is a Donsker class, in particular,

converges weakly in to a Brownian bridge B(F(x)) .

See also[edit]


  1. ^ Mojirsheibani, M. (2007). "Nonparametric curve estimation with missing data: A general empirical process approach". Journal of Statistical Planning and Inference. 137 (9): 2733–2758. doi:10.1016/j.jspi.2006.02.016.
  2. ^ Wolfowitz, J. (1954). "Generalization of the Theorem of Glivenko-Cantelli". The Annals of Mathematical Statistics. 25: 131–138. doi:10.1214/aoms/1177728852.

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