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Level set Cox processes

Hildeman, Anders, 1984 (author)
Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences,Chalmers tekniska högskola,Chalmers University of Technology
Bolin, David, 1983 (author)
Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences,Chalmers tekniska högskola,Chalmers University of Technology
Wallin, Jonas, 1981 (author)
Lund University,Lunds universitet,Statistiska institutionen,Ekonomihögskolan,Department of Statistics,Lund University School of Economics and Management, LUSEM
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Illian, J. B. (author)
University of St Andrews
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 (creator_code:org_t)
Elsevier BV, 2018
2018
English.
In: Spatial Statistics. - : Elsevier BV. - 2211-6753. ; 28, s. 169-193
  • Journal article (peer-reviewed)
Abstract Subject headings
Close  
  • An extension of the popular log-Gaussian Cox process (LGCP) model for spatial point patterns is proposed for data exhibiting fundamentally different behaviors in different subregions of the spatial domain. The aim of the analyst might be either to identify and classify these regions, to perform kriging, or to derive some properties of the parameters driving the random field in one or several of the subregions. The extension is based on replacing the latent Gaussian random field in the LGCP by a latent spatial mixture model specified using a categorically valued random field. This classification is defined through level set operations on a Gaussian random field and allows for standard stationary covariance structures, such as the Matern family, to be used to model random fields with some degree of general smoothness but also occasional and structured sharp discontinuities. A computationally efficient MCMC method is proposed for Bayesian inference and we show consistency of finite dimensional approximations of the model. Finally, the model is fitted to point pattern data derived from a tropical rainforest on Barro Colorado island, Panama. We show that the proposed model is able to capture behavior for which inference based on the standard LGCP is biased. (C) 2018 Elsevier B.V. All rights reserved.

Subject headings

NATURVETENSKAP  -- Matematik (hsv//swe)
NATURAL SCIENCES  -- Mathematics (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Teknisk mekanik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Applied Mechanics (hsv//eng)
NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Keyword

Point process
Cox process
Level set inversion
Classification
Gaussian fields
inverse problems
mcmc methods
approximation
diversity
inference
models
Geology
Mathematics
Remote Sensing
Cox process

Publication and Content Type

ref (subject category)
art (subject category)

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