Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

On statistical inference for the random set generated Cox process with set-marking.

Antti Penttinen1, Aki Niemi

  • 1Department of Mathematics and Statistics, P.O. Box 35 FIN-40014 University of Jyväskylä, Finland. penttine@maths.jyu.fi

Biometrical Journal. Biometrische Zeitschrift
|May 5, 2007
PubMed
Summary

This study introduces a random set marked Cox process for modeling spatial data with environmental heterogeneity. It derives key properties and offers practical estimation methods for forestry applications.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Enhanced resolution optoacoustic microscopy using a picosecond high repetition rate Q-switched microchip laser.

Journal of biomedical optics·2022
Same author

Dual-curing resin cement with colour indicator for adhesively cemented restorations to dental tissues: Change of colour by curing and some physical properties.

Saudi journal of biological sciences·2020
Same author

Bayesian modeling of the evolution of male height in 18th century Finland from incomplete data.

Economics and human biology·2013
Same author

Cancer risk near a polluted river in Finland.

Environmental health perspectives·2004

Area of Science:

  • Spatial statistics
  • Stochastic geometry
  • Point process theory

Background:

  • Cox point processes model spatial data with random intensity.
  • Environmental heterogeneity influences point distribution.
  • Random closed sets can model geometric patterns in spatial data.

Purpose of the Study:

  • To introduce and analyze a random set marked Cox process.
  • To derive the second-order properties of this new process.
  • To develop practical estimation methods for forestry applications.

Main Methods:

  • Utilizing random closed sets to generate random intensity for Cox processes.
  • Deriving second-order properties of the random set marked Cox process.
  • Developing estimation methods for area fraction, covariance, and point densities.

Related Experiment Videos

Main Results:

  • The second-order properties of the random set marked Cox process are derived.
  • Practical estimation methods for key parameters are proposed.
  • The model is validated with simulated and forestry data.

Conclusions:

  • The random set marked Cox process provides a flexible framework for spatial modeling.
  • The derived properties and estimation methods are valuable for analyzing spatially heterogeneous data.
  • This approach has practical implications for forestry and other fields with geometric spatial patterns.