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# Empirical process

The study of empirical processes is a branch of mathematical statistics and a sub-area of probability theory. It is a generalization of the central limit theorem for empirical measures.

Definition

It is known that under certain conditions empirical measures $P_n$ uniformly converge to the probability measure "P" (see Glivenko-Cantelli theorem). The theory of "Empirical processes" provides the rate of this convergence.

A centered and scaled version of the empirical measure is the signed measure:$G_n\left(A\right)=sqrt\left\{n\right\}\left(P_n\left(A\right)-P\left(A\right)\right)$It induces map on measurable functions "f" given by

:$fmapsto G_n f=sqrt\left\{n\right\}\left(P_n-P\right)f=sqrt\left\{n\right\}left\left(frac\left\{1\right\}\left\{n\right\}sum_\left\{i=1\right\}^n f\left(X_i\right)-mathbb\left\{E\right\}f ight\right)$

By the central limit theorem, $G_n\left(A\right)$ 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", $G_nf$ converges in distribution to a normal random variable $N\left(0,mathbb\left\{E\right\}\left(f-mathbb\left\{E\right\}f\right)^2\right)$, provided that $mathbb\left\{E\right\}f$ and $mathbb\left\{E\right\}f^2$ exist.

Definition: is called an "empirical process" indexed by $mathcal\left\{C\right\}$, a collection of measurable subsets of "S".: is called an "empirical process" indexed by $mathcal\left\{F\right\}$, a collection of measurable functions from "S" to $mathbb\left\{R\right\}$.

A significant result in the area of empirical processes is Donsker's theorem. It has led to a study of the "Donsker classes" such that empirical processes indexed by these classes converge weakly to a certain Gaussian process. It can be shown that the Donsker classes are Glivenko-Cantelli, the converse is not true in general.

Example

As an example, consider empirical distribution functions. For real-valued iid random variables $X_1,X_n,...$ they are given by

:$F_n\left(x\right)=P_n\left(\left(-infty,x\right] \right)=P_nI_\left\{\left(-infty,x\right] \right\}.$

In this case, empirical processes are indexed by a class $mathcal\left\{C\right\}=\left\{\left(-infty,x\right] :xinmathbb\left\{R\right\}\right\}.$ It has been shown that $mathcal\left\{C\right\}$ is a Donsker class, in particular,:$sqrt\left\{n\right\}\left(F_n\left(x\right)-F\left(x\right)\right)$ converges weakly in $ell^infty\left(mathbb\left\{R\right\}\right)$ to a Brownian bridge "B(F(x))".

References

* P. Billingsley, Probability and Measure, John Wiley and Sons, New York, third edition, 1995.
* M.D. Donsker, Justification and extension of Doob's heuristic approach to the Kolmogorov-Smirnov theorems, Annals of Mathematical Statistics, 23:277-281, 1952.
* R.M. Dudley, Central limit theorems for empirical measures, Annals of Probability, 6(6): 899-929, 1978.
* R.M. Dudley, Uniform Central Limit Theorems, Cambridge Studies in Advanced Mathematics, 63, Cambridge University Press, Cambridge, UK, 1999.
* M.R. Kosorok, Inroduction to Empirical Processes and Semiparametric Inference, Springer, New York, 2008.
* Aad W. van der Vaart and Jon A. Wellner,Weak Convergence and Empirical Processes: With Applications to Statistics, 2nd ed., Springer, 2000. ISBN 978-0387946405
* J. Wolfowitz, Generalization of the theorem of Glivenko-Cantelli. Annals of Mathematical Statistics, 25, 131-138, 1954.

* [http://www.stat.yale.edu/~pollard/Iowa/ Empirical Processes: Theory and Applications] , by David Pollard, a textbook available online.
* [http://www.bios.unc.edu/~kosorok/current.pdf Introduction to Empirical Processes and Semiparametric Inference] , by Michael Kosorok, another textbook available online.

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