Bayesian Analysis and Matching Errors in Closed Population Capture Recapture Models

Show simple item record

dc.contributor.advisor Dr. Leonard A. Stefanski, Committee Co-Chair en_US
dc.contributor.advisor Dr. Sujit K. Ghosh, Committee Co-Chair en_US
dc.contributor.advisor Dr. Kenneth Pollock, Committee Member en_US
dc.contributor.advisor Dr. Cavell Brownie, Committee Member en_US
dc.contributor.author Gosky, Ross Matthew en_US
dc.date.accessioned 2010-04-02T18:26:37Z
dc.date.available 2010-04-02T18:26:37Z
dc.date.issued 2005-03-01 en_US
dc.identifier.other etd-08172004-113554 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/3087
dc.description.abstract Capture-Recapture models are used to estimate the unknown sizes of animal populations. When the population is closed, with constant size, during the study, eight standard models exist for estimating population size. These models allow for variation in animal capture probabilities due to time effects, heterogeneity among animals, and behavioral effects after the first capture. Our research focuses on two areas: 1. Using Bayesian statistical modeling, we present versions of these eight models. We explore the use of Akaike's Information Criterion (AIC), and the Deviance Information Criterion (DIC) as tools for selecting the appropriate model for a given dataset. Through simulation, we show that AIC performs well in model selection. 2. A new, non-invasive method of capturing animals is to substitute captures of DNA profiles, through sources such as hair samples, for live animal captures. However, DNA profiles of close relatives may not be distinguishable from each other, and some animals in the population may not be uniquely identifiable. This problem leads to negative bias in estimating population size. We present a hierarchical statistical model which accounts for this type of matching error, leading to more accurate estimation of population size. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject Bayesian en_US
dc.subject Model Selection en_US
dc.subject Matching Errors en_US
dc.subject mark-recapture en_US
dc.subject capture-recapture en_US
dc.title Bayesian Analysis and Matching Errors in Closed Population Capture Recapture Models en_US
dc.degree.name PhD en_US
dc.degree.level dissertation en_US
dc.degree.discipline Statistics en_US


Files in this item

Files Size Format View
etd.pdf 873.4Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record