A characterization of order statistic point processes that are mixed Poisson processes and mixed sample processes simultaneously

1985 ◽  
Vol 22 (02) ◽  
pp. 314-323
Author(s):  
A. Deffner ◽  
E. Haeusler
Keyword(s):  
Order Statistic ◽  
Real Line ◽  
Time Scale ◽  
Point Processes ◽  
Present Note ◽  
Poisson Processes ◽  
Scale Transformation ◽  
The Real ◽  
Mixed Sample

The results of Nawrotzki (1962), Feigin (1979) and Puri (1982) show that the class of all point processes (on the real line) with the order statistic property consists of all mixed Poisson processes up to a time-scale transformation, and of all mixed sample processes. The present note characterizes those order statistic point processes that are mixed Poisson processes and mixed sample processes simultaneously.

A characterization of order statistic point processes that are mixed Poisson processes and mixed sample processes simultaneously

1985 ◽  
Vol 22 (2) ◽  
pp. 314-323 ◽  
Author(s):  
A. Deffner ◽  
E. Haeusler
Keyword(s):  
Order Statistic ◽  
Real Line ◽  
Time Scale ◽  
Point Processes ◽  
Present Note ◽  
Poisson Processes ◽  
Scale Transformation ◽  
The Real ◽  
Mixed Sample

The results of Nawrotzki (1962), Feigin (1979) and Puri (1982) show that the class of all point processes (on the real line) with the order statistic property consists of all mixed Poisson processes up to a time-scale transformation, and of all mixed sample processes. The present note characterizes those order statistic point processes that are mixed Poisson processes and mixed sample processes simultaneously.

On the characterization of point processes with the order statistic property

1979 ◽  
Vol 16 (2) ◽  
pp. 297-304 ◽  
Author(s):  
Paul D. Feigin
Keyword(s):  
Order Statistic ◽  
Real Line ◽  
Point Processes ◽  
Martingale Theory ◽  
The Real ◽  
Probabilistic Proof

We provide a probabilistic proof of the characterization of point processes (on the real line) with the order statistic property. The characterization is used to investigate the homogeneity of such processes and is also related to the martingale theory associated with point processes.

On the characterization of point processes with the order statistic property without the moment condition

1982 ◽  
Vol 19 (01) ◽  
pp. 39-51 ◽  
Author(s):  
Prem S. Puri
Keyword(s):  
Order Statistic ◽  
Point Processes ◽  
Poisson Processes ◽  
Scale Transformation ◽  
Moment Condition ◽  
The Moment ◽  
First Moment ◽  
Multivariate Point ◽  
Multivariate Point Processes

The paper characterizes point processes with the order statistic property without the unnecessary condition of finiteness of the first moment of the process, a condition imposed by previous researchers. It shows that the class of these processes is composed only of mixed Poisson processes up to a time-scale transformation and of the mixed sample processes. It also introduces a multivariate analog of the order statistic property and characterizes completely the class of multivariate point processes with this property.

On the characterization of point processes with the order statistic property

1979 ◽  
Vol 16 (02) ◽  
pp. 297-304 ◽  
Author(s):  
Paul D. Feigin
Keyword(s):  
Order Statistic ◽  
Real Line ◽  
Point Processes ◽  
Martingale Theory ◽  
The Real ◽  
Probabilistic Proof

We provide a probabilistic proof of the characterization of point processes (on the real line) with the order statistic property. The characterization is used to investigate the homogeneity of such processes and is also related to the martingale theory associated with point processes.

On the characterization of point processes with the order statistic property without the moment condition

1982 ◽  
Vol 19 (1) ◽  
pp. 39-51 ◽  
Author(s):  
Prem S. Puri
Keyword(s):  
Order Statistic ◽  
Point Processes ◽  
Poisson Processes ◽  
Scale Transformation ◽  
Moment Condition ◽  
The Moment ◽  
First Moment ◽  
Multivariate Point ◽  
Multivariate Point Processes

The paper characterizes point processes with the order statistic property without the unnecessary condition of finiteness of the first moment of the process, a condition imposed by previous researchers. It shows that the class of these processes is composed only of mixed Poisson processes up to a time-scale transformation and of the mixed sample processes. It also introduces a multivariate analog of the order statistic property and characterizes completely the class of multivariate point processes with this property.

scholarly journals The probability generating functional

1972 ◽  
Vol 14 (4) ◽  
pp. 448-466 ◽  
Author(s):  
M. Westcott
Keyword(s):  
Point Process ◽  
Real Line ◽  
Point Processes ◽  
Generating Functional ◽  
The Real ◽  
The Subject

This paper is concerned with certain aspects of the theory and application of the probability generating functional (p.g.fl) of a point process on the real line. Interest in point processes has increased rapidly during the last decade and a number of different approaches to the subject have been expounded (see for example [6], [11], [15], [17], [20], [25], [27], [28]). It is hoped that the present development using the p.g.ff will calrify and unite some of these viewpoints and provide a useful tool for solution of particular problems.

Dependent thinning of point processes

1980 ◽  
Vol 17 (04) ◽  
pp. 987-995 ◽  
Author(s):  
Valerie Isham
Keyword(s):  
Poisson Process ◽  
Point Process ◽  
Real Line ◽  
Point Processes ◽  
Compound Poisson Process ◽  
Second Order ◽  
Binary Variables ◽  
Compound Poisson ◽  
The Real ◽  
Markov Sequence

A point process, N, on the real line, is thinned using a k -dependent Markov sequence of binary variables, and is rescaled. Second-order properties of the thinned process are described when k = 1. For general k, convergence to a compound Poisson process is demonstrated.

Sufficient conditions for long-range count dependence of stationary point processes on the real line

2001 ◽  
Vol 38 (2) ◽  
pp. 570-581 ◽  
Author(s):  
Rafał Kulik ◽  
Ryszard Szekli
Keyword(s):  
Long Range ◽  
Real Line ◽  
Stationary Point ◽  
Point Processes ◽  
Sufficient Conditions ◽  
Sufficient Condition ◽  
Second Moment ◽  
The Real ◽  
Palm Measure ◽  
Stationary Point Processes

Daley and Vesilo (1997) introduced long-range count dependence (LRcD) for stationary point processes on the real line as a natural augmentation of the classical long-range dependence of the corresponding interpoint sequence. They studied LRcD for some renewal processes and some output processes of queueing systems, continuing the previous research on such processes of Daley (1968), (1975). Subsequently, Daley (1999) showed that a necessary and sufficient condition for a stationary renewal process to be LRcD is that under its Palm measure the generic lifetime distribution has infinite second moment. We show that point processes dominating, in a sense of stochastic ordering, LRcD point processes are LRcD, and as a corollary we obtain that for arbitrary stationary point processes with finite intensity a sufficient condition for LRcD is that under Palm measure the interpoint distances are positively dependent (associated) with infinite second moment. We give many examples of LRcD point processes, among them exchangeable, cluster, moving average, Wold, semi-Markov processes and some examples of LRcD point processes with finite second Palm moment of interpoint distances. These examples show that, in general, the condition of infiniteness of the second moment is not necessary for LRcD. It is an open question whether the infinite second Palm moment of interpoint distances suffices to make a stationary point process LRcD.

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