Two-scale spatial models for binary data
Cécile Hardouin () and
Noel Cressie
Additional contact information
Cécile Hardouin: MODAL’X, Université Paris Nanterre
Noel Cressie: University of Wollongong
Statistical Methods & Applications, 2018, vol. 27, issue 1, No 1, 24 pages
Abstract:
Abstract A spatial lattice model for binary data is constructed from two spatial scales linked through conditional probabilities. A coarse grid of lattice locations is specified, and all remaining locations (which we call the background) capture fine-scale spatial dependence. Binary data on the coarse grid are modelled with an autologistic distribution, conditional on the binary process on the background. The background behaviour is captured through a hidden Gaussian process after a logit transformation on its Bernoulli success probabilities. The likelihood is then the product of the (conditional) autologistic probability distribution and the hidden Gaussian–Bernoulli process. The parameters of the new model come from both spatial scales. A series of simulations illustrates the spatial-dependence properties of the model and likelihood-based methods are used to estimate its parameters. Presence–absence data of corn borers in the roots of corn plants are used to illustrate how the model is fitted.
Keywords: Auto-logistic model; EM algorithm; Gaussian process; Hierarchical statistical model; Laplace approximation; Spatial odds-ratio (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://link.springer.com/10.1007/s10260-017-0391-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:27:y:2018:i:1:d:10.1007_s10260-017-0391-1
Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2
DOI: 10.1007/s10260-017-0391-1
Access Statistics for this article
Statistical Methods & Applications is currently edited by Tommaso Proietti
More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().