559 research outputs found
Density estimation for grouped data with application to line transect sampling
Line transect sampling is a method used to estimate wildlife populations,
with the resulting data often grouped in intervals. Estimating the density from
grouped data can be challenging. In this paper we propose a kernel density
estimator of wildlife population density for such grouped data. Our method uses
a combined cross-validation and smoothed bootstrap approach to select the
optimal bandwidth for grouped data. Our simulation study shows that with the
smoothing parameter selected with this method, the estimated density from
grouped data matches the true density more closely than with other approaches.
Using smoothed bootstrap, we also construct bias-adjusted confidence intervals
for the value of the density at the boundary. We apply the proposed method to
two grouped data sets, one from a wooden stake study where the true density is
known, and the other from a survey of kangaroos in Australia.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS307 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Meta-analysis of functional neuroimaging data using Bayesian nonparametric binary regression
In this work we perform a meta-analysis of neuroimaging data, consisting of
locations of peak activations identified in 162 separate studies on emotion.
Neuroimaging meta-analyses are typically performed using kernel-based methods.
However, these methods require the width of the kernel to be set a priori and
to be constant across the brain. To address these issues, we propose a fully
Bayesian nonparametric binary regression method to perform neuroimaging
meta-analyses. In our method, each location (or voxel) has a probability of
being a peak activation, and the corresponding probability function is based on
a spatially adaptive Gaussian Markov random field (GMRF). We also include
parameters in the model to robustify the procedure against miscoding of the
voxel response. Posterior inference is implemented using efficient MCMC
algorithms extended from those introduced in Holmes and Held [Bayesian Anal. 1
(2006) 145--168]. Our method allows the probability function to be locally
adaptive with respect to the covariates, that is, to be smooth in one region of
the covariate space and wiggly or even discontinuous in another. Posterior
miscoding probabilities for each of the identified voxels can also be obtained,
identifying voxels that may have been falsely classified as being activated.
Simulation studies and application to the emotion neuroimaging data indicate
that our method is superior to standard kernel-based methods.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS523 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Accounting for spatial correlation in the scan statistic
The spatial scan statistic is widely used in epidemiology and medical studies
as a tool to identify hotspots of diseases. The classical spatial scan
statistic assumes the number of disease cases in different locations have
independent Poisson distributions, while in practice the data may exhibit
overdispersion and spatial correlation. In this work, we examine the behavior
of the spatial scan statistic when overdispersion and spatial correlation are
present, and propose a modified spatial scan statistic to account for that.
Some theoretical results are provided to demonstrate that ignoring the
overdispersion and spatial correlation leads to an increased rate of false
positives, which is verified through a simulation study. Simulation studies
also show that our modified procedure can substantially reduce the rate of
false alarms. Two data examples involving brain cancer cases in New Mexico and
chickenpox incidence data in France are used to illustrate the practical
relevance of the modified procedure.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS129 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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