84 research outputs found
Bayesian Semiparametric Hierarchical Empirical Likelihood Spatial Models
We introduce a general hierarchical Bayesian framework that incorporates a
flexible nonparametric data model specification through the use of empirical
likelihood methodology, which we term semiparametric hierarchical empirical
likelihood (SHEL) models. Although general dependence structures can be readily
accommodated, we focus on spatial modeling, a relatively underdeveloped area in
the empirical likelihood literature. Importantly, the models we develop
naturally accommodate spatial association on irregular lattices and irregularly
spaced point-referenced data. We illustrate our proposed framework by means of
a simulation study and through three real data examples. First, we develop a
spatial Fay-Herriot model in the SHEL framework and apply it to the problem of
small area estimation in the American Community Survey. Next, we illustrate the
SHEL model in the context of areal data (on an irregular lattice) through the
North Carolina sudden infant death syndrome (SIDS) dataset. Finally, we analyze
a point-referenced dataset from the North American Breeding Bird survey that
considers dove counts for the state of Missouri. In all cases, we demonstrate
superior performance of our model, in terms of mean squared prediction error,
over standard parametric analyses.Comment: 29 pages, 3 figue
Spatial Fay-Herriot Models for Small Area Estimation with Functional Covariates
The Fay-Herriot (FH) model is widely used in small area estimation and uses
auxiliary information to reduce estimation variance at undersampled locations.
We extend the type of covariate information used in the FH model to include
functional covariates, such as social-media search loads or remote-sensing
images (e.g., in crop-yield surveys). The inclusion of these functional
covariates is facilitated through a two-stage dimension-reduction approach that
includes a Karhunen-Lo\`{e}ve expansion followed by stochastic search variable
selection. Additionally, the importance of modeling spatial autocorrelation has
recently been recognized in the FH model; our model utilizes the intrinsic
conditional autoregressive class of spatial models in addition to functional
covariates. We demonstrate the effectiveness of our approach through simulation
and analysis of data from the American Community Survey. We use Google Trends
searches over time as functional covariates to analyze relative changes in
rates of percent household Spanish-speaking in the eastern half of the United
States.Comment: 26 pages, 5 figure
Air and water pollution over time and industries with stochastic dominance
We employ a stochastic dominance (SD) approach to analyze the components that contribute to environmental degradation over time. The variables include countries\u2019 greenhouse gas (GHG) emissions and water pollution. Our approach is based on pair-wise SD tests. First, we study the dynamic progress of each separate variable over time, from 1990 to 2005, within 5-year horizons. Then, pair-wise SD tests are used to study the major industry contributors to the overall GHG emissions and water pollution at any given time, to uncover the industry which contributes the most to total emissions and water pollution. While CO2 emissions increased in the first order SD sense over 15 years, water pollution increased in a second-order SD sense. Electricity and heat production were the major contributors to the CO2 emissions, while the food industry gradually became the major water polluting
industry over time.
SD sense over 15 years, water pollution increased in
a second-order SD sense. Electricity and heat production
were the major contributors to the CO2 emissions, while
the food industry gradually
Representing spatial dependence and spatial discontinuity in ecological epidemiology: a scale mixture approach
The Neutrophil's Eye-View: Inference and Visualisation of the Chemoattractant Field Driving Cell Chemotaxis In Vivo
As we begin to understand the signals that drive chemotaxis in vivo, it is becoming clear that there is a complex interplay of chemotactic factors, which changes over time as the inflammatory response evolves. New animal models such as transgenic lines of zebrafish, which are near transparent and where the neutrophils express a green fluorescent protein, have the potential to greatly increase our understanding of the chemotactic process under conditions of wounding and infection from video microscopy data. Measurement of the chemoattractants over space (and their evolution over time) is a key objective for understanding the signals driving neutrophil chemotaxis. However, it is not possible to measure and visualise the most important contributors to in vivo chemotaxis, and in fact the understanding of the main contributors at any particular time is incomplete. The key insight that we make in this investigation is that the neutrophils themselves are sensing the underlying field that is driving their action and we can use the observations of neutrophil movement to infer the hidden net chemoattractant field by use of a novel computational framework. We apply the methodology to multiple in vivo neutrophil recruitment data sets to demonstrate this new technique and find that the method provides consistent estimates of the chemoattractant field across the majority of experiments. The framework that we derive represents an important new methodology for cell biologists investigating the signalling processes driving cell chemotaxis, which we label the neutrophils eye-view of the chemoattractant field
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