Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probit models for capture-recapture data subject to imperfect detection,
  individual heterogeneity and misidentification   As noninvasive sampling techniques for animal populations have become more
popular, there has been increasing interest in the development of
capture-recapture models that can accommodate both imperfect detection and
misidentification of individuals (e.g., due to genotyping error). However,
current methods do not allow for individual variation in parameters, such as
detection or survival probability. Here we develop misidentification models for
capture-recapture data that can simultaneously account for temporal variation,
behavioral effects and individual heterogeneity in parameters. To facilitate
Bayesian inference using our approach, we extend standard probit regression
techniques to latent multinomial models where the dimension and zeros of the
response cannot be observed. We also present a novel Metropolis-Hastings within
Gibbs algorithm for fitting these models using Markov chain Monte Carlo. Using
closed population abundance models for illustration, we re-visit a DNA
capture-recapture population study of black bears in Michigan, USA and find
evidence of misidentification due to genotyping error, as well as temporal,
behavioral and individual variation in detection probability. We also estimate
a salamander population of known size from laboratory experiments evaluating
the effectiveness of a marking technique commonly used for amphibians and fish.
Our model was able to reliably estimate the size of this population and
provided evidence of individual heterogeneity in misidentification probability
that is attributable to variable mark quality. Our approach is more
computationally demanding than previously proposed methods, but it provides the
flexibility necessary for a much broader suite of models to be explored while
properly accounting for uncertainty introduced by misidentification and
imperfect detection. In the absence of misidentification, our probit
formulation also provides a convenient and efficient Gibbs sampler for Bayesian
analysis of traditional closed population capture-recapture data.

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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: cs
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The categories of this paper are: math