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Understanding patterns of aggregation in count data

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dc.contributor.advisor Njuho, Peter Sebatjane, Phuti 2017-02-24T10:38:55Z 2017-02-24T10:38:55Z 2016-06
dc.identifier.citation Sebatjane, Phuti (2016) Understanding patterns of aggregation in count data, University of South Africa, Pretoria, <> en
dc.description.abstract The term aggregation refers to overdispersion and both are used interchangeably in this thesis. In addressing the problem of prevalence of infectious parasite species faced by most rural livestock farmers, we model the distribution of faecal egg counts of 15 parasite species (13 internal parasites and 2 ticks) common in sheep and goats. Aggregation and excess zeroes is addressed through the use of generalised linear models. The abundance of each species was modelled using six different distributions: the Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), zero-altered Poisson (ZAP) and zero-altered negative binomial (ZANB) and their fit was later compared. Excess zero models (ZIP, ZINB, ZAP and ZANB) were found to be a better fit compared to standard count models (Poisson and negative binomial) in all 15 cases. We further investigated how distributional assumption a↵ects aggregation and zero inflation. Aggregation and zero inflation (measured by the dispersion parameter k and the zero inflation probability) were found to vary greatly with distributional assumption; this in turn changed the fixed-effects structure. Serial autocorrelation between adjacent observations was later taken into account by fitting observation driven time series models to the data. Simultaneously taking into account autocorrelation, overdispersion and zero inflation proved to be successful as zero inflated autoregressive models performed better than zero inflated models in most cases. Apart from contribution to the knowledge of science, predictability of parasite burden will help farmers with effective disease management interventions. Researchers confronted with the task of analysing count data with excess zeroes can use the findings of this illustrative study as a guideline irrespective of their research discipline. Statistical methods from model selection, quantifying of zero inflation through to accounting for serial autocorrelation are described and illustrated. en
dc.format.extent 1 online resource (viii, 101 leaves) : illustrations en
dc.language.iso en en
dc.subject Aggregations en
dc.subject Autoregressive models en
dc.subject Akaike information criterion en
dc.subject Correlation en
dc.subject Count data en
dc.subject Exponential family en
dc.subject Generalised linear models en
dc.subject Goats en
dc.subject Internal parasites en
dc.subject Hosts en
dc.subject Negative binomial distribution en
dc.subject Overdispersion en
dc.subject Poisson distribution en
dc.subject Sheep en
dc.subject Time series en
dc.subject Zero inflation en
dc.subject.ddc 519.537
dc.subject.lcsh Correlation (Statistics) en
dc.subject.lcsh Akaike Information Criterion en
dc.subject.lcsh Exponential functions en
dc.subject.lcsh Negative binomial distribution en
dc.subject.lcsh Poisson distribution en
dc.subject.lcsh Livestock -- Parasites en
dc.subject.lcsh Time-series analysis en
dc.subject.lcsh Binomial distribution en
dc.title Understanding patterns of aggregation in count data en
dc.type Dissertation en
dc.description.department Statistics en M.Sc. (Statistics) en

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