NDLI: Statistical inference for Gibbs point processes based on field observations

Content Provider	Springer Nature Link
Author	Comas, C. Mateu, J.
Copyright Year	2010
Abstract	Forest inventories are mostly based on field observations, and complete records of spatial tree coordinates are seldom taken. The lack of individual coordinates prevents the use of well stablised statistical inference tools based on the likelihood function. However, the Takacs–Fiksel approach, based on equating two expectations derived from different measures, can be used routinely without any measurement of tree coordinates, just by considering nearest neighbour measurements and the counting of trees at some random positions. Despite this, little attention has been paid to the Takacs–Fiksel method in terms of the type of test function and the type of field observation data considered. Motivated by problems based on field observations, we present a simulation study to analyse and illustrate the quality of the parameter estimates for this estimation approach under distinct simulated scenarios, where several test functions and distinct forest sampling designs are taken into account. Indeed, the type of the chosen test function affects the resulting estimates in terms of the forest field observation considered.
Starting Page	287
Ending Page	300
Page Count	14
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	25
Issue Number	2
e-ISSN	14363259
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-10-19
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Forest field observations Forest sampling Monte Carlo simulation Nearest neighbour measurements Pairwise interaction point processes Spatial point patterns Takacs–Fiksel method Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution Computational Intelligence Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Mathematical Applications in Earth Sciences Probability Theory and Stochastic Processes Math. Application in Environmental Science
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Takacs Fiksel method for stationary marked Gibbs point processes

Exploring the ability of the Pettitt method for detecting change point by Monte Carlo simulation

Multi-point source identification of sudden water pollution accidents in surface waters based on differential evolution and Metropolis–Hastings–Markov Chain Monte Carlo

A field sampling strategy for semivariogram inference of fractures in rock outcrops

A nonhomogeneous Poisson process geostatistical model

Monte Carlo method applied to modeling copper transport in river sediments

Importance sampling for efficient modelling of hydraulic loads in the Rhine–Meuse delta

Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management

Joint Monte Carlo and possibilistic simulation for flood damage assessment

Statistical inference for Gibbs point processes based on field observations

Similar Documents

Takacs Fiksel method for stationary marked Gibbs point processes

Exploring the ability of the Pettitt method for detecting change point by Monte Carlo simulation

Multi-point source identification of sudden water pollution accidents in surface waters based on differential evolution and Metropolis–Hastings–Markov Chain Monte Carlo

A field sampling strategy for semivariogram inference of fractures in rock outcrops

A nonhomogeneous Poisson process geostatistical model

Monte Carlo method applied to modeling copper transport in river sediments

Importance sampling for efficient modelling of hydraulic loads in the Rhine–Meuse delta

Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management

Joint Monte Carlo and possibilistic simulation for flood damage assessment

Statistical inference for Gibbs point processes based on field observations