422 research outputs found
A spatial capture-recapture model for territorial species
Advances in field techniques have lead to an increase in spatially-referenced
capture-recapture data to estimate a species' population size as well as other
demographic parameters and patterns of space usage. Statistical models for
these data have assumed that the number of individuals in the population and
their spatial locations follow a homogeneous Poisson point process model, which
implies that the individuals are uniformly and independently distributed over
the spatial domain of interest. In many applications there is reason to
question independence, for example when species display territorial behavior.
In this paper, we propose a new statistical model which allows for dependence
between locations to account for avoidance or territorial behavior. We show via
a simulation study that accounting for this can improve population size
estimates. The method is illustrated using a case study of small mammal
trapping data to estimate avoidance and population density of adult female
field voles (Microtus agrestis) in northern England
A latent factor model for spatial data with informative missingness
A large amount of data is typically collected during a periodontal exam.
Analyzing these data poses several challenges. Several types of measurements
are taken at many locations throughout the mouth. These spatially-referenced
data are a mix of binary and continuous responses, making joint modeling
difficult. Also, most patients have missing teeth. Periodontal disease is a
leading cause of tooth loss, so it is likely that the number and location of
missing teeth informs about the patient's periodontal health. In this paper we
develop a multivariate spatial framework for these data which jointly models
the binary and continuous responses as a function of a single latent spatial
process representing general periodontal health. We also use the latent spatial
process to model the location of missing teeth. We show using simulated and
real data that exploiting spatial associations and jointly modeling the
responses and locations of missing teeth mitigates the problems presented by
these data.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS278 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
- …