Limits for the superposition of m-dimensional point processes

1972 ◽  
Vol 9 (2) ◽  
pp. 462-465 ◽  
Author(s):  
Ward Whitt
Keyword(s):  
Weak Convergence ◽  
Function Space ◽  
Point Processes ◽  
Asymptotic Independence ◽  
Independent Components

To obtain a limit with independent components in the superposition of m-dimensional point processes, a condition corresponding to asymptotic independence must be included. When this condition is relaxed, convergence to limits with dependent components is possible. In either case, convergence of finite distributions alone implies tightness and thus weak convergence in the function space D[0, ∞) × … × D[0, ∞).

Limits for the superposition of m-dimensional point processes

1972 ◽  
Vol 9 (02) ◽  
pp. 462-465
Author(s):  
Ward Whitt
Keyword(s):  
Weak Convergence ◽  
Function Space ◽  
Point Processes ◽  
Asymptotic Independence ◽  
Independent Components

To obtain a limit with independent components in the superposition of m-dimensional point processes, a condition corresponding to asymptotic independence must be included. When this condition is relaxed, convergence to limits with dependent components is possible. In either case, convergence of finite distributions alone implies tightness and thus weak convergence in the function space D[0, ∞) × … × D[0, ∞).

Point processes, regular variation and weak convergence

1986 ◽  
Vol 18 (01) ◽  
pp. 66-138 ◽  
Author(s):  
Sidney I. Resnick
Keyword(s):  
Weak Convergence ◽  
Function Space ◽  
Regular Variation ◽  
Point Processes ◽  
Sufficient Condition ◽  
Space Setting

A method is reviewed for proving weak convergence in a function-space setting when regular variation is a sufficient condition. Point processes and weak convergence techniques involving continuity arguments play a central role. The method is dimensionless and holds computations to a minimum. Many applications of the methods to processes derived from sums and maxima are given.

Point processes, regular variation and weak convergence

1986 ◽  
Vol 18 (1) ◽  
pp. 66-138 ◽  
Author(s):  
Sidney I. Resnick
Keyword(s):  
Weak Convergence ◽  
Function Space ◽  
Regular Variation ◽  
Point Processes ◽  
Sufficient Condition ◽  
Space Setting

A method is reviewed for proving weak convergence in a function-space setting when regular variation is a sufficient condition. Point processes and weak convergence techniques involving continuity arguments play a central role. The method is dimensionless and holds computations to a minimum. Many applications of the methods to processes derived from sums and maxima are given.

A note on disjoint-occurrence inequalities for marked Poisson point processes

1996 ◽  
Vol 33 (2) ◽  
pp. 420-426 ◽  
Author(s):  
J. van den Berg
Keyword(s):  
Weak Convergence ◽  
Point Processes ◽  
Poisson Point Processes ◽  
Bk Inequality

For (marked) Poisson point processes we give, for increasing events, a new proof of the analog of the BK inequality. In contrast to other proofs, which use weak-convergence arguments, our proof is ‘direct' and requires no extra topological conditions on the events. Apart from some well-known properties of Poisson point processes, the proof is self-contained.

Asymptotic independence of counts in isotropic planar point processes of phase-type

2000 ◽  
Vol 32 (2) ◽  
pp. 363-375
Author(s):  
Marie-Ange Remiche
Keyword(s):  
Poisson Process ◽  
Arbitrary Shape ◽  
Point Processes ◽  
The Other ◽  
Asymptotic Independence ◽  
Planar Point ◽  
Phase Type ◽  
Disjoint Sets ◽  
The Mean ◽  
The One

The isotropic planar point processes of phase-type are natural generalizations of the Poisson process on the plane. On the one hand, those processes are isotropic and stationary for the mean count, as in the case of the Poisson process. On the other hand, they exhibit dependence of counts in disjoint sets. In a recent paper, we have proved that the number of points in a square window has a Poisson distribution asymptotically as the window is located far away from the origin of the process. We extend our work to the case of a window of arbitrary shape.

scholarly journals On the weak convergence of stochastic processes with embedded point processes

1988 ◽  
Vol 20 (2) ◽  
pp. 473-475 ◽  
Author(s):  
Panagiotis Konstantopoulos ◽  
Jean Walrand
Keyword(s):  
Stochastic Process ◽  
Weak Convergence ◽  
Stochastic Processes ◽  
Point Process ◽  
Real Line ◽  
Continuous Time ◽  
Point Processes ◽  
Mixing Condition ◽  
The Real ◽  
Palm Distribution

We consider a stochastic process in continuous time and two point processes on the real line, all jointly stationary. We show that under a certain mixing condition the values of the process at the points of the second point process converge weakly under the Palm distribution with respect to the first point process, and we identify the limit. This result is a supplement to two other known results which are mentioned below.

The distribution of the convex hull of a Gaussian sample

1980 ◽  
Vol 17 (03) ◽  
pp. 686-695 ◽  
Author(s):  
William F. Eddy
Keyword(s):  
Weak Convergence ◽  
Convex Hull ◽  
Poisson Point Process ◽  
Point Processes ◽  
Convex Sets ◽  
Continuous Mapping ◽  
Continuous Functions ◽  
Space Of Continuous Functions ◽  
Limit Process ◽  
Gaussian Sample

The distribution of the convex hull of a random sample ofd-dimensional variables is described by embedding the collection of convex sets into the space of continuous functions on the unit sphere. Weak convergence of the normalized convex hull of a circular Gaussian sample to a process with extreme-value marginal distributions is demonstrated. The proof shows that an underlying sequence of point processes converges to a Poisson point process and then applies the continuous mapping theorem. Several properties of the limit process are determined.

On the superposition of m-dimensional point processes

1968 ◽  
Vol 5 (1) ◽  
pp. 169-176 ◽  
Author(s):  
Erhan Çinlar
Keyword(s):  
Poisson Process ◽  
Point Processes ◽  
Natural Extension ◽  
Renewal Processes ◽  
Independent Vector ◽  
One Dimensional ◽  
Independent Components ◽  
Markov Renewal Processes ◽  
The One ◽  
Vector Valued

Consider n independent vector valued point processes. Superposition is defined component by component as a natural extension of the definition for the one-dimensional case. Under proper conditions as n → ∞, it is shown that the superposed process is a many-dimensional Poisson process with independent components. The results are applied to the superposition of Markov renewal processes.

Sign in / Sign up

Export Citation Format

Share Document