# Empirical process

In probability theory, an empirical process is a stochastic process that describes the proportion of objects in a system in a given state. For a process in a discrete state space a population continuous time Markov chain[1][2] or Markov population model[3] is a process which counts the number of objects in a given state (without rescaling). 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.[4]

## Definition

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

${\displaystyle F_{n}(x)={\frac {1}{n}}\sum _{i=1}^{n}I_{(-\infty ,x]}(X_{i}),}$

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.[5]

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

${\displaystyle G_{n}(A)={\sqrt {n}}(P_{n}(A)-P(A))}$

It induces a map on measurable functions f given by

${\displaystyle f\mapsto G_{n}f={\sqrt {n}}(P_{n}-P)f={\sqrt {n}}\left({\frac {1}{n}}\sum _{i=1}^{n}f(X_{i})-\mathbb {E} f\right)}$

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

Definition

${\displaystyle {\bigl (}G_{n}(c){\bigr )}_{c\in {\mathcal {C}}}}$ is called an empirical process indexed by ${\displaystyle {\mathcal {C}}}$, a collection of measurable subsets of S.
${\displaystyle {\bigl (}G_{n}f{\bigr )}_{f\in {\mathcal {F}}}}$ is called an empirical process indexed by ${\displaystyle {\mathcal {F}}}$, a collection of measurable functions from S to ${\displaystyle \mathbb {R} }$.

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.

## Example

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

${\displaystyle F_{n}(x)=P_{n}((-\infty ,x])=P_{n}I_{(-\infty ,x]}.}$

In this case, empirical processes are indexed by a class ${\displaystyle {\mathcal {C}}=\{(-\infty ,x]:x\in \mathbb {R} \}.}$ It has been shown that ${\displaystyle {\mathcal {C}}}$ is a Donsker class, in particular,

${\displaystyle {\sqrt {n}}(F_{n}(x)-F(x))}$ converges weakly in ${\displaystyle \ell ^{\infty }(\mathbb {R} )}$ to a Brownian bridge B(F(x)) .

## References

1. ^ Bortolussi, L.; Hillston, J.; Latella, D.; Massink, M. (2013). "Continuous approximation of collective systems behaviour: A tutorial" (PDF). Performance Evaluation. 70 (5): 317. doi:10.1016/j.peva.2013.01.001.
2. ^ Stefanek, A.; Hayden, R. A.; Mac Gonagle, M.; Bradley, J. T. (2012). "Mean-Field Analysis of Markov Models with Reward Feedback". Analytical and Stochastic Modeling Techniques and Applications. Lecture Notes in Computer Science. 7314. p. 193. doi:10.1007/978-3-642-30782-9_14. ISBN 978-3-642-30781-2.
3. ^ Dayar, T. R.; Hermanns, H.; Spieler, D.; Wolf, V. (2011). "Bounding the equilibrium distribution of Markov population models". Numerical Linear Algebra with Applications. 18 (6): 931. arXiv:1007.3130. doi:10.1002/nla.795.
4. ^ 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.
5. ^ Wolfowitz, J. (1954). "Generalization of the Theorem of Glivenko-Cantelli". The Annals of Mathematical Statistics. 25: 131–138. doi:10.1214/aoms/1177728852.