A marked point process perspective in fitting spatial point process models

Research output: Contribution to journalArticle

Abstract

This paper discusses a new perspective in fitting spatial point process models. Specifically the spatial point process of interest is treated as a marked point process where at each observed event x a stochastic process M (x ; t), 0 < t < r, is defined. Each mark process M (x ; t) is compared with its expected value, say F (t ; θ), to produce a discrepancy measure at x, where θ is a set of unknown parameters. All individual discrepancy measures are combined to define an overall measure which will then be minimized to estimate the unknown parameters. The proposed approach can be easily applied to data with sample size commonly encountered in practice. Simulations and an application to a real data example demonstrate the efficacy of the proposed approach.

Original languageEnglish (US)
Pages (from-to)2143-2153
Number of pages11
JournalJournal of Statistical Planning and Inference
Volume138
Issue number7
DOIs
StatePublished - Jul 1 2008
Externally publishedYes

Fingerprint

Spatial Point Process
Marked Point Process
Random processes
Process Model
Unknown Parameters
Discrepancy
Expected Value
Efficacy
Stochastic Processes
Sample Size
Estimate
Demonstrate
Process model
Marked point process
Point process
Simulation
Sample size
Stochastic processes
Expected value

Keywords

  • K-function
  • Marked point process
  • Spatial point process

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Statistics and Probability

Cite this

A marked point process perspective in fitting spatial point process models. / Guan, Yongtao.

In: Journal of Statistical Planning and Inference, Vol. 138, No. 7, 01.07.2008, p. 2143-2153.

Research output: Contribution to journalArticle

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