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dc.contributor.authorOedekoven, C.S.
dc.contributor.authorLaake, J.L.
dc.contributor.authorSkaug, H.J.
dc.date.accessioned2016-03-22T00:01:20Z
dc.date.available2016-03-22T00:01:20Z
dc.date.issued2015-12
dc.identifier.citationOedekoven , C S , Laake , J L & Skaug , H J 2015 , ' Distance sampling with a random scale detection function ' Environmental and Ecological Statistics , vol. 22 , no. 4 , pp. 725-737 . DOI: 10.1007/s10651-015-0316-9en
dc.identifier.issn1352-8505
dc.identifier.otherPURE: 180107219
dc.identifier.otherPURE UUID: 24b1107a-38ce-4c44-b79c-8f58192d327d
dc.identifier.otherScopus: 84947489330
dc.identifier.urihttp://hdl.handle.net/10023/8454
dc.descriptionCornelia Oedekoven was supported by a studentship jointly funded by the University of St Andrews and EP-SRC, through the National Centre for Statistical Ecology (EP-SRC Grant EP/C522702/1). Hans Skaug thanks the Center for Stock Assessment Research for facilitating his visit to University of California, Santa Cruz.en
dc.description.abstractDistance sampling was developed to estimate wildlife abundance from observational surveys with uncertain detection in the search area. We present novel analysis methods for estimating detection probabilities that make use of random effects models to allow for unmodeled heterogeneity in detection. The scale parameter of the half-normal detection function is modeled by means of an intercept plus an error term varying with detections, normally distributed with zero mean and unknown variance. In contrast to conventional distance sampling methods, our approach can deal with long-tailed detection functions without truncation. Compared to a fixed effect covariate approach, we think of the random effect as a covariate with unknown values and integrate over the random effect. We expand the random scale to a mixed scale model by adding fixed effect covariates. We analyzed simulated data with large sample sizes to demonstrate that the code performs correctly for random and mixed effect models. We also generated replicate simulations with more practical sample sizes ((Formula presented.)) and compared the random scale half-normal with the hazard rate detection function. As expected each estimation model was best for different simulation models. We illustrate the mixed effect modeling approach using harbor porpoise vessel survey data where the mixed effect model provided an improved model fit in comparison to a fixed effect model with the same covariates. We propose that a random or mixed effect model of the detection function scale be adopted as one of the standard approaches for fitting detection functions in distance sampling.en
dc.language.isoeng
dc.relation.ispartofEnvironmental and Ecological Statisticsen
dc.rights© Springer Science+Business Media New York 2015. The final publication is available at Springer via http://dx.doi.org/10.1007/s10651-015-0316-9en
dc.subjectAbundance estimationen
dc.subjectAD Model Builderen
dc.subjectHalf-normalen
dc.subjectHarbor porpoise detectionsen
dc.subjectHeterogeneity in detection probabilitiesen
dc.subjectMixed effectsen
dc.subjectQH301 Biologyen
dc.subjectQL Zoologyen
dc.subjectHA Statisticsen
dc.subjectDASen
dc.subject.lccQH301en
dc.subject.lccQLen
dc.subject.lccHAen
dc.titleDistance sampling with a random scale detection functionen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doihttps://doi.org/10.1007/s10651-015-0316-9
dc.description.statusPeer revieweden
dc.date.embargoedUntil22-03-20
dc.identifier.urlhttp://static-content.springer.com/esm/art%3A10.1007%2Fs10651-015-0316-9/MediaObjects/10651_2015_316_MOESM1_ESM.pdf


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