11,871 research outputs found
Time-varying coefficient models for the analysis of air pollution and health outcome data
In this article a time-varying coefficient model is developed to examine the relationship between adverse health and short-term (acute) exposure to air pollution. This model allows the relative risk to evolve over time, which may be due to an interaction with temperature, or from a change in the composition of pollutants, such as particulate matter, over time. The model produces a smooth estimate of these time-varying effects, which are not constrained to follow a fixed parametric form set by the investigator. Instead, the shape is estimated from the data using penalized natural cubic splines. Poisson regression models, using both quasi-likelihood and Bayesian techniques, are developed, with estimation performed using an iteratively re-weighted least squares procedure and Markov chain Monte Carlo simulation, respectively. The efficacy of the methods to estimate different types of time-varying effects are assessed via a simulation study, and the models are then applied to data from four cities that were part of the National Morbidity, Mortality, and Air Pollution Study
Global Optimization for Future Gravitational Wave Detectors' Sites
We consider the optimal site selection of future generations of gravitational
wave detectors. Previously, Raffai et al. optimized a 2-detector network with a
combined figure of merit. This optimization was extended to networks with more
than two detectors in a limited way by first fixing the parameters of all other
component detectors. In this work we now present a more general optimization
that allows the locations of all detectors to be simultaneously chosen. We
follow the definition of Raffai et al. on the metric that defines the
suitability of a certain detector network. Given the locations of the component
detectors in the network, we compute a measure of the network's ability to
distinguish the polarization, constrain the sky localization and reconstruct
the parameters of a gravitational wave source. We further define the
`flexibility index' for a possible site location, by counting the number of
multi-detector networks with a sufficiently high Figure of Merit that include
that site location. We confirm the conclusion of Raffai et al., that in terms
of flexibility index as defined in this work, Australia hosts the best
candidate site to build a future generation gravitational wave detector. This
conclusion is valid for either a 3-detector network or a 5-detector network.
For a 3-detector network site locations in Northern Europe display a comparable
flexibility index to sites in Australia. However for a 5-detector network,
Australia is found to be a clearly better candidate than any other location.Comment: 30 pages, 23 figures, 2 table
Construction of Bayesian Deformable Models via Stochastic Approximation Algorithm: A Convergence Study
The problem of the definition and the estimation of generative models based
on deformable templates from raw data is of particular importance for modelling
non aligned data affected by various types of geometrical variability. This is
especially true in shape modelling in the computer vision community or in
probabilistic atlas building for Computational Anatomy (CA). A first coherent
statistical framework modelling the geometrical variability as hidden variables
has been given by Allassonni\`ere, Amit and Trouv\'e (JRSS 2006). Setting the
problem in a Bayesian context they proved the consistency of the MAP estimator
and provided a simple iterative deterministic algorithm with an EM flavour
leading to some reasonable approximations of the MAP estimator under low noise
conditions. In this paper we present a stochastic algorithm for approximating
the MAP estimator in the spirit of the SAEM algorithm. We prove its convergence
to a critical point of the observed likelihood with an illustration on images
of handwritten digits
BEAST: Bayesian evolutionary analysis by sampling trees
<p>Abstract</p> <p>Background</p> <p>The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented.</p> <p>Results</p> <p>BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at <url>http://beast-mcmc.googlecode.com/</url> under the GNU LGPL license.</p> <p>Conclusion</p> <p>BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.</p
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