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Generalised additive modelling and zero inflated count data

Welsh, Alan; Barry, Simon C

Description

This paper describes a flexible method for modelling zero inflated count data which are typically found when trying to model and predict species distributions. Zero inflated data are defined as data that has a larger proportion of zeros than expected from pure count (Poisson) data. The standard methodology is to model the data in two steps, first modelling the association between the presence and absence of a species and the available covariates and second, modelling the relationship between...[Show more]

dc.contributor.authorWelsh, Alan
dc.contributor.authorBarry, Simon C
dc.date.accessioned2015-12-13T22:22:44Z
dc.identifier.issn0304-3800
dc.identifier.urihttp://hdl.handle.net/1885/72395
dc.description.abstractThis paper describes a flexible method for modelling zero inflated count data which are typically found when trying to model and predict species distributions. Zero inflated data are defined as data that has a larger proportion of zeros than expected from pure count (Poisson) data. The standard methodology is to model the data in two steps, first modelling the association between the presence and absence of a species and the available covariates and second, modelling the relationship between abundance and the covariates, conditional on the organism being present. The approach in this paper extends previous work to incorporate the use of Generalized Additive Models (GAM) in the modelling steps. The paper develops the link and variance functions needed for the use of GAM with zero inflated data. It then demonstrates the performance of the models using data on stem counts of Eucalyptus mannifera in a region of South East Australia.
dc.publisherElsevier
dc.sourceEcological Modelling
dc.subjectKeywords: ecological modeling; statistical analysis; Eucalyptus; Zeros Abundance models; Count data; Distribution modelling; Generalized additive models; Prediction; Statistical models; Zero inflated data
dc.titleGeneralised additive modelling and zero inflated count data
dc.typeJournal article
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.citationvolume157
dc.date.issued2002
local.identifier.absfor010405 - Statistical Theory
local.identifier.ariespublicationMigratedxPub3224
local.type.statusPublished Version
local.contributor.affiliationWelsh, Alan, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationBarry, Simon C, Bureau of Rural Sciences
local.description.embargo2037-12-31
local.bibliographicCitation.startpage179
local.bibliographicCitation.lastpage188
local.identifier.doi10.1016/S0304-3800(02)00194-1
dc.date.updated2015-12-11T07:58:39Z
local.identifier.scopusID2-s2.0-0037202448
CollectionsANU Research Publications

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