Brownian excursion

{{Short description|Stochastic process}}

Image:BrownExcursion1D.svg

In probability theory a Brownian excursion process (BPE) is a stochastic process that is closely related to a Wiener process (or Brownian motion). Realisations of Brownian excursion processes are essentially just realizations of a Wiener process selected to satisfy certain conditions. In particular, a Brownian excursion process is a Wiener process conditioned to be positive and to take the value 0 at time 1. Alternatively, it is a Brownian bridge process conditioned to be positive. BEPs are important because, among other reasons, they naturally arise as the limit process of a number of conditional functional central limit theorems.Durrett, Iglehart: Functionals of Brownian Meander and Brownian Excursion, (1975)

Constructions

File:Brownian bridge as a union of excursions.svg

A Brownian excursion process, e, is a Wiener process (or Brownian motion) conditioned to be positive and to take the value 0 at time 1. Alternatively, it is a Brownian bridge process conditioned to be positive. Although note that, since the probability for an unrestricted Brownian bridge to be positive is zero, the conditioning requires care.

Another representation of a Brownian excursion e in terms of a Brownian motion process W (due to Paul Lévy and noted by Kiyosi Itô and Henry P. McKean, Jr.Itô and McKean (1974, page 75))

is in terms of the last time \tau_{-} that W hits zero before time 1 and the first time \tau_{+} that Brownian motion W hits zero after time 1:

:

\{ e(t) : \ {0 \le t \le 1} \} \ \stackrel{d}{=} \ \left \{ \frac

W((1-t) \tau_{-} + t \tau_{+} )
{\sqrt{\tau_+ - \tau_{-}}} : \ 0 \le t \le 1 \right \},

where the square root is due to the square root self-similarity of Wiener process. That is, \sqrt a W_{t/a} is a Wiener process for any fixed constant a > 0.File:Vervaat's transformation.svg

Let \tau_m be the time that a Brownian bridge process W_0 achieves its minimum on [0, 1]. Vervaat (1979) shows that

:

\{ e(t) : \ {0\le t \le 1} \} \ \stackrel{d}{=} \ \left \{ W_0 ( \tau_m + t \bmod 1) - W_0 (\tau_m ): \ 0 \le t \le 1 \right \} .

This is sometimes called Vervaat's transformation.

Properties

Vervaat's representation of a Brownian excursion has several consequences for various functions of e. In particular:

:M_{+} \equiv \sup_{0 \le t \le 1} e(t) \ \stackrel{d}{=} \ \sup_{0 \le t \le 1} W_0 (t) - \inf_{0 \le t \le 1} W_0 (t) ,

(this can also be derived by explicit calculationsChung (1976)Kennedy (1976)) and

: \int_0^1 e(t) \, dt \ \stackrel{d}{=} \

\int_0^1 W_0 (t) \, dt - \inf_{0 \le t \le 1} W_0 (t) .

The following result holds:Durrett and Iglehart (1977)

:E M_+ = \sqrt{\pi/2} \approx 1.25331 \ldots, \,

and the following values for the second moment and variance can be calculated by the exact form of the distribution and density:

:E M_+^2 \approx 1.64493 \ldots \ , \ \

\operatorname{Var}(M_+) \approx 0.0741337 \ldots.

Groeneboom (1989), Lemma 4.2 gives an expression for the Laplace transform of (the density) of \int_0^1 e(t) \, dt . A formula for a certain double transform of the distribution of this area integral is given by Louchard (1984).

Groeneboom (1983) and Pitman (1983) give decompositions of Brownian motion W in terms of i.i.d Brownian excursions and the least concave majorant (or greatest convex minorant) of W.

For an introduction to Itô's general theory of Brownian excursions and the Itô Poisson process of excursions, see Revuz and Yor (1994), chapter XII.

Connections and applications

With probability 1, a Wiener process is continuous, which means the set on which it is non-zero is an open subset of the real line, thus it is the union of countably many Brownian excursions.

The Brownian excursion area

:A_+ \equiv \int_0^1 e(t) \, dt

arises in connection with the enumeration of connected graphs, many other problems in combinatorial theory; see e.g.{{Cite journal |doi = 10.1002/jgt.3190010407|title = The number of connected sparsely edged graphs|journal = Journal of Graph Theory|volume = 1|issue = 4|pages = 317–330|year = 1977|last1 = Wright|first1 = E. M.|authorlink1=E. M. Wright}}{{Cite journal |doi = 10.1002/jgt.3190040409|title = The number of connected sparsely edged graphs. III. Asymptotic results|journal = Journal of Graph Theory|volume = 4|issue = 4|pages = 393–407|year = 1980|last1 = Wright|first1 = E. M.|authorlink1=E. M. Wright}}{{cite journal | author = Spencer J | year = 1997 | title = Enumerating graphs and Brownian motion | journal = Communications on Pure and Applied Mathematics| volume = 50 | issue = 3| pages = 291–294 | doi=10.1002/(sici)1097-0312(199703)50:3<291::aid-cpa4>3.0.co;2-6}}{{Cite journal | doi=10.1214/07-PS104| title=Brownian excursion area, Wright's constants in graph enumeration, and other Brownian areas| journal=Probability Surveys| volume=4| pages=80–145| year=2007| last1=Janson| first1=Svante| bibcode=2007arXiv0704.2289J| arxiv=0704.2289| s2cid=14563292}}{{cite journal | author = Flajolet, P. |author2=Louchard, G. | year = 2001 | title = Analytic variations on the Airy distribution | journal = Algorithmica | volume = 31 | issue = 3| pages = 361–377 | doi=10.1007/s00453-001-0056-0| citeseerx = 10.1.1.27.3450 |s2cid=6522038 }} and the limit distribution of the Betti numbers of certain varieties in cohomology theory.{{cite journal | author = Reineke M | year = 2005 | title = Cohomology of noncommutative Hilbert schemes | journal = Algebras and Representation Theory | volume = 8 | issue = 4| pages = 541–561 | doi=10.1007/s10468-005-8762-y| arxiv = math/0306185| s2cid = 116587916 }} Takacs (1991a) shows that A_+ has density

:f_{A_+} (x) = \frac{2 \sqrt{6}}{x^2} \sum_{j=1}^\infty v_j^{2/3} e^{-v_j} U\left ( - \frac{5}{6} , \frac{4}{3}; v_j \right ) \ \ \text{ with } \ \ v_j = \frac{2 |a_j|^3}{27x^2}

where a_j are the zeros of the Airy function and U is the confluent hypergeometric function.

Janson and Louchard (2007) show that

:f_{A_+} (x) \sim \frac{72 \sqrt{6}}{\sqrt{\pi}} x^2 e^{- 6 x^2} \ \ \text{ as } \ \ x \rightarrow \infty,

and

:P(A_+ > x) \sim \frac{6 \sqrt{6}}{\sqrt{\pi}} x e^{- 6x^2} \ \ \text{ as } \ \ x \rightarrow \infty.

They also give higher-order expansions in both cases.

Janson (2007) gives moments of A_+ and many other area functionals. In particular,

:

E (A_+) = \frac{1}{2} \sqrt{\frac{\pi}{2}}, \ \ E(A_+^2) = \frac{5}{12} \approx 0.416666 \ldots, \ \ \operatorname{Var}(A_+) = \frac{5}{12} - \frac{\pi}{8} \approx .0239675 \ldots \ .

Brownian excursions also arise in connection with

queuing problems,{{cite journal | author = Iglehart D. L. | year = 1974 | title = Functional central limit theorems for random walks conditioned to stay positive | journal = The Annals of Probability| volume = 2 | issue = 4| pages = 608–619 | doi=10.1214/aop/1176996607| doi-access = free }}

railway traffic,{{cite journal | author = Takacs L | year = 1991a | title = A Bernoulli excursion and its various applications | journal = Advances in Applied Probability | volume = 23 | issue = 3| pages = 557–585 | doi=10.1017/s0001867800023739}}{{cite journal | author = Takacs L | year = 1991b | title = On a probability problem connected with railway traffic | journal = Journal of Applied Mathematics and Stochastic Analysis| volume = 4 | pages = 263–292 | doi = 10.1155/S1048953391000011 | doi-access = free }} and the heights of random rooted binary trees.{{cite journal | author = Takacs L | year = 1994 | title = On the Total Heights of Random Rooted Binary Trees| journal = Journal of Combinatorial Theory, Series B| volume = 61 | issue = 2| pages = 155–166 | doi=10.1006/jctb.1994.1041| doi-access = free}}

Related processes

Notes

References

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{{Stochastic processes}}

{{DEFAULTSORT:Brownian Excursion}}

Category:Wiener process