# Empirical process

A stochastic process constructed from a sample and the corresponding probability measure. Let $X _ {1} \dots X _ {n} , \dots$ be a sequence of independent random elements with common law $P$, taking values in a measurable space $( S, {\mathcal S} )$. The empirical measure $P _ {n}$ of the first $n$ $X _ {i}$ s is the discrete random measure that places mass ${1 / n }$ on each such $X _ {i}$:

$$P _ {n} ( C ) = { \frac{1}{n} } \# \left \{ {1 \leq i \leq n } : {X _ {i} \in C } \right \} , \quad C \in {\mathcal S}.$$

Obviously, $n P _ {n} ( C )$ is binomially distributed with parameters $n$ and $P ( C )$( cf. Binomial distribution). Hence ${\mathsf E} P _ {n} ( C ) = P ( C )$, ${\mathsf P} ( {\lim\limits } _ {n \rightarrow \infty } P _ {n} ( C ) = P ( C ) ) = 1$, and $\sqrt n ( P _ {n} ( C ) - P ( C ) )$ converges in distribution, as $n \rightarrow \infty$, to a centred normal random variable with variance $P ( C ) ( 1 - P ( C ) )$( cf. Convergence in distribution). Therefore it is natural to define an empirical process indexed by sets by

$$\tag{a1 } \alpha _ {n} ( C ) = \sqrt n ( P _ {n} ( C ) - P ( C ) ) , \quad C \in {\mathcal C},$$

where ${\mathcal C} \subset {\mathcal S}$. If $( S, {\mathcal S} ) = ( \mathbf R, {\mathcal B} )$ and ${\mathcal C} = \{ {( - \infty,x ] } : {x \in \mathbf R } \}$, one writes $F _ {n} ( x ) = P _ {n} ( ( - \infty,x ] )$ for the empirical distribution function, and the empirical process specializes to the classical empirical process

$$\tag{a2 } \alpha _ {n} ( x ) = \sqrt n ( F _ {n} ( x ) - F ( x ) ) , \quad x \in \mathbf R,$$

where $F ( x ) = {\mathsf P} ( X _ {i} \leq x )$, $x \in \mathbf R$, is the distribution function of the elements $X _ {i}$. Replacing sets by their indicator functions leads, more generally, to the definition of an empirical process indexed by functions:

$$\tag{a3 } \alpha _ {n} ( f ) = \sqrt n ( P _ {n} ( f ) - P ( f ) ) , \quad f \in {\mathcal F},$$

where

$$P _ {n} ( f ) = \int\limits _ { S } f {d P _ {n} } = { \frac{1}{n} } \sum _ {i = 1 } ^ { n } f ( X _ {i} ) ,$$

$$P ( f ) = \int\limits _ { S } f {d P } = {\mathsf E} f ( X _ {i} ) ,$$

and ${\mathcal F}$ is a suitable class of measurable functions from $S$ to $\mathbf R$.

The main aim of the theory of empirical processes is to obtain results uniformly in $C$, $x$ or $f$; in particular, Glivenko–Cantelli-type theorems, central limit theorems, laws of the iterated logarithm, and probability inequalities (cf., e.g., Empirical distribution; Central limit theorem; Law of the iterated logarithm). (Measurability issues will be disregarded in the sequel.) The concept of a Vapnik–Chervonenkis class plays an important role in set-indexed situations. E.g., if ${\mathcal C}$ is a Vapnik–Chervonenkis class, then for every probability measure $P$ on $( S, {\mathcal S} )$,

$$\tag{a4 } \sup _ {C \in {\mathcal C} } \left | {P _ {n} ( C ) - P ( C ) } \right | \rightarrow 0 \textrm{ a.s. } ,$$

and $\alpha _ {n} ( C )$, $C \in {\mathcal C}$, converges weakly (see [a10] and Weak topology) to $B _ {P} ( C )$, $C \in {\mathcal C}$, a centred, bounded Gaussian process, which is uniformly continuous (with respect to the pseudometric $d$ defined by $d ( C _ {1} ,C _ {2} ) = P ( C _ {1} \Delta C _ {2} )$) and has covariance structure

$${\mathsf E} B _ {P} ( C _ {1} ) B _ {P} ( C _ {2} ) = P ( C _ {1} \cap C _ {2} ) - P ( C _ {1} ) P ( C _ {2} ) ,$$

$$C _ {1} ,C _ {2} \in {\mathcal C}.$$

For the classical empirical process in (a2), this limiting process specializes to $B \circ F$, where $B$ is a Brownian bridge (cf. Non-parametric methods in statistics). A sharp version of the first result is the following: (a4) holds if and only if

$$\tag{a5 } {\mathsf P} roman \AAh {\lim\limits } { \frac{ { \mathop{\rm log} } \Delta ^ {\mathcal C} ( X _ {1} \dots X _ {n} ) }{n} } = 0,$$

where

$$\Delta ^ {\mathcal C} ( X _ {1} \dots X _ {n} ) = \# \left \{ {C \cap \{ X _ {1} \dots X _ {n} \} } : {C \in {\mathcal C} } \right \}$$

(see Vapnik–Chervonenkis class). A corresponding sharp version of the central limit theorem exists too; essentially the only change is that the $n$ in the denominator of (a5) has to be replaced by $\sqrt n$ to obtain an "if and only if" condition for the central limit theorem. Other useful concepts in connection with empirical processes are various notions of entropy, see [a12], [a13], [a9], [a10]. Also, for the function-indexed process in (a3), the analogues of (a4) and the central limit theorem above have been studied thoroughly, see [a5], [a9], [a10].

For the classical empirical process in (a2), approximation theorems which yield a rate of convergence in the central limit theorem are extremely useful: A sequence of Brownian bridges $\{ {B _ {n} ( t ) } : {t \in [ 0,1 ] } \}$, $n = 2,3, \dots$, can be constructed such that for all $\lambda > 0$

$${\mathsf P} \left ( \sup _ {x \in \mathbf R } \left | {\alpha _ {n} ( x ) - B _ {n} ( F ( x ) ) } \right | > { \frac{12 { \mathop{\rm log} } n + \lambda }{\sqrt n } } \right ) \leq 2e ^ {- {\lambda / 6 } } .$$

A similar, only slightly less sharp, result can be obtained for the situation where the joint distribution of the $B _ {n}$ s is known, i.e., the $B _ {n}$ s are defined by means of one single Kiefer process, see [a3].

Empirical and related processes have many applications in many different subfields of probability theory and (non-parametric) statistics.

#### References

 [a1] K.S. Alexander, "Rates of growth and sample moduli for weighted empirical processes indexed by sets" Probab. Th. Rel. Fields , 75 (1987) pp. 379–423 [a2] M. Csörgő, S. Csörgő, L. Horváth, D.M. Mason, "Weighted empirical and quantile processes" Ann. of Probab. , 14 (1986) pp. 31–85 [a3] M. Csörgő, P. Révész, "Strong approximations in probability and statistics" , Acad. Press (1981) [a4] P. Deheuvels, D.M. Mason, "Functional laws of the iterated logarithm for the increments of empirical and quantile processes" Ann. of Probab. , 20 (1992) pp. 1248–1287 [a5] R.M. Dudley, "Universal Donsker classes and metric entropy" Ann. of Probab. , 15 (1987) pp. 1306–1326 [a6] J.H.J. Einmahl, "The a.s. behavior of the weighted empirical process and the LIL for the weighted tail empirical process" Ann. of Probab. , 20 (1992) pp. 681–695 [a7] E. Giné, "Empirical processes and applications: an overview" Bernoulli , 2 (1996) pp. 1–28 [a8] P. Massart, "The tight constant in the Dvoretzky–Kiefer–Wolfowitz inequality" Ann. of Probab. , 18 (1990) pp. 1269–1283 [a9] D. Pollard, "Convergence of stochastic processes" , Springer (1984) [a10] A. Sheehy, J.A. Wellner, "Uniform Donsker classes of functions" Ann. of Probab. , 20 (1992) pp. 1983–2030 [a11] G.R. Shorack, J.A. Wellner, "Empirical processes with applications to statistics" , Wiley (1986) [a12] K.S. Alexander, "Probability inequalities for empirical processes and a law of the iterated logarithm" Ann. of Probab. , 12 (1984) pp. 1041–1067 [a13] K.S. Alexander, "Correction: Probability inequalities for empirical processes and a law of the iterated logarithm" Ann. of Probab. , 15 (1987) pp. 428–430
How to Cite This Entry:
Empirical process. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Empirical_process&oldid=46819
This article was adapted from an original article by J.H.J. Einmahl (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article