Likelihood-based inference for clustered line transect data

Rasmus Waagepetersen, Tore Schweder

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

14 Citationer (Scopus)

Abstrakt

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
OriginalsprogEngelsk
TidsskriftJournal of Agricultural, Biological, and Environmental Statistics
Vol/bind11
Udgave nummer3
Sider (fra-til)264-279
Antal sider16
ISSN1085-7117
StatusUdgivet - 2006

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