Likelihood-based inference for clustered line transect data
The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference is...
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ftalborgunivpubl:oai:pure.atira.dk:publications/1d192b30-7968-11db-805f-000ea68e967b 2024-09-15T18:25:19+00:00 Likelihood-based inference for clustered line transect data Waagepetersen, Rasmus Schweder, Tore 2006 https://vbn.aau.dk/da/publications/1d192b30-7968-11db-805f-000ea68e967b eng eng https://vbn.aau.dk/da/publications/1d192b30-7968-11db-805f-000ea68e967b info:eu-repo/semantics/restrictedAccess Waagepetersen , R & Schweder , T 2006 , ' Likelihood-based inference for clustered line transect data ' , Journal of Agricultural, Biological, and Environmental Statistics , vol. 11 , no. 3 , pp. 264-279 . article 2006 ftalborgunivpubl 2024-07-10T12:27:45Z The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference is implemented using markov chain Monte Carlo (MCMC) methods to obtain efficient estimates of spatial clustering parameters. Uncertainty is addressed using parametric bootstrap or by consideration of posterior distributions in a Bayesian setting. Maximum likelihood estimation and Bayesian inference are compared in an example concerning minke whales in the northeast Atlantic. Udgivelsesdato: SEP The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference is implemented using markov chain Monte Carlo (MCMC) methods to obtain efficient estimates of spatial clustering parameters. Uncertainty is addressed using parametric bootstrap or by consideration of posterior distributions in a Bayesian setting. Maximum likelihood estimation and Bayesian inference are compared in an example concerning minke whales in the northeast Atlantic. Article in Journal/Newspaper Northeast Atlantic Aalborg University's Research Portal |
institution |
Open Polar |
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Aalborg University's Research Portal |
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ftalborgunivpubl |
language |
English |
description |
The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference is implemented using markov chain Monte Carlo (MCMC) methods to obtain efficient estimates of spatial clustering parameters. Uncertainty is addressed using parametric bootstrap or by consideration of posterior distributions in a Bayesian setting. Maximum likelihood estimation and Bayesian inference are compared in an example concerning minke whales in the northeast Atlantic. Udgivelsesdato: SEP The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference is implemented using markov chain Monte Carlo (MCMC) methods to obtain efficient estimates of spatial clustering parameters. Uncertainty is addressed using parametric bootstrap or by consideration of posterior distributions in a Bayesian setting. Maximum likelihood estimation and Bayesian inference are compared in an example concerning minke whales in the northeast Atlantic. |
format |
Article in Journal/Newspaper |
author |
Waagepetersen, Rasmus Schweder, Tore |
spellingShingle |
Waagepetersen, Rasmus Schweder, Tore Likelihood-based inference for clustered line transect data |
author_facet |
Waagepetersen, Rasmus Schweder, Tore |
author_sort |
Waagepetersen, Rasmus |
title |
Likelihood-based inference for clustered line transect data |
title_short |
Likelihood-based inference for clustered line transect data |
title_full |
Likelihood-based inference for clustered line transect data |
title_fullStr |
Likelihood-based inference for clustered line transect data |
title_full_unstemmed |
Likelihood-based inference for clustered line transect data |
title_sort |
likelihood-based inference for clustered line transect data |
publishDate |
2006 |
url |
https://vbn.aau.dk/da/publications/1d192b30-7968-11db-805f-000ea68e967b |
genre |
Northeast Atlantic |
genre_facet |
Northeast Atlantic |
op_source |
Waagepetersen , R & Schweder , T 2006 , ' Likelihood-based inference for clustered line transect data ' , Journal of Agricultural, Biological, and Environmental Statistics , vol. 11 , no. 3 , pp. 264-279 . |
op_relation |
https://vbn.aau.dk/da/publications/1d192b30-7968-11db-805f-000ea68e967b |
op_rights |
info:eu-repo/semantics/restrictedAccess |
_version_ |
1810465821408886784 |