3,127 research outputs found
Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings
In low-resource settings, prevalence mapping relies on empirical prevalence
data from a finite, often spatially sparse, set of surveys of communities
within the region of interest, possibly supplemented by remotely sensed images
that can act as proxies for environmental risk factors. A standard
geostatistical model for data of this kind is a generalized linear mixed model
with binomial error distribution, logistic link and a combination of
explanatory variables and a Gaussian spatial stochastic process in the linear
predictor. In this paper, we first review statistical methods and software
associated with this standard model, then consider several methodological
extensions whose development has been motivated by the requirements of specific
applications. These include: methods for combining randomised survey data with
data from non-randomised, and therefore potentially biased, surveys;
spatio-temporal extensions; spatially structured zero-inflation. Throughout, we
illustrate the methods with disease mapping applications that have arisen
through our involvement with a range of African public health programmes.Comment: Submitte
On The Inverse Geostatistical Problem of Inference on Missing Locations
The standard geostatistical problem is to predict the values of a spatially
continuous phenomenon, say, at locations using data
where is the realization at location of
, or of a random variable that is stochastically related to
. In this paper we address the inverse problem of predicting the
locations of observed measurements . We discuss how knowledge of the
sampling mechanism can and should inform a prior specification, say,
for the joint distribution of the measurement locations , and propose an efficient Metropolis-Hastings algorithm for
drawing samples from the resulting predictive distribution of the missing
elements of . An important feature in many applied settings is that this
predictive distribution is multi-modal, which severely limits the usefulness of
simple summary measures such as the mean or median. We present two simulated
examples to demonstrate the importance of the specification for , and
analyze rainfall data from Paran\'a State, Brazil to show how, under additional
assumptions, an empirical of estimate of can be used when no prior
information on the sampling design is available.Comment: Under revie
Suppression of zinc dendrites in zinc electrode power cells
Addition of various tetraalkyl quarternary ammonium salts, to alkaline zincate electrolyte of cell, prevents formation of zinc dendrites during charging of zinc electrode. Electrode capacity is not impaired and elimination of dendrites prolongs cell life
INLA or MCMC? A Tutorial and Comparative Evaluation for Spatial Prediction in log-Gaussian Cox Processes
We investigate two options for performing Bayesian inference on spatial
log-Gaussian Cox processes assuming a spatially continuous latent field: Markov
chain Monte Carlo (MCMC) and the integrated nested Laplace approximation
(INLA). We first describe the device of approximating a spatially continuous
Gaussian field by a Gaussian Markov random field on a discrete lattice, and
present a simulation study showing that, with careful choice of parameter
values, small neighbourhood sizes can give excellent approximations. We then
introduce the spatial log-Gaussian Cox process and describe MCMC and INLA
methods for spatial prediction within this model class. We report the results
of a simulation study in which we compare MALA and the technique of
approximating the continuous latent field by a discrete one, followed by
approximate Bayesian inference via INLA over a selection of 18 simulated
scenarios. The results question the notion that the latter technique is both
significantly faster and more robust than MCMC in this setting; 100,000
iterations of the MALA algorithm running in 20 minutes on a desktop PC
delivered greater predictive accuracy than the default \verb=INLA= strategy,
which ran in 4 minutes and gave comparative performance to the full Laplace
approximation which ran in 39 minutes.Comment: This replaces the previous version of the report. The new version
includes results from an additional simulation study, and corrects an error
in the implementation of the INLA-based method
Julian Ernst Besag, 26 March 1945 -- 6 August 2010, a biographical memoir
Julian Besag was an outstanding statistical scientist, distinguished for his
pioneering work on the statistical theory and analysis of spatial processes,
especially conditional lattice systems. His work has been seminal in
statistical developments over the last several decades ranging from image
analysis to Markov chain Monte Carlo methods. He clarified the role of
auto-logistic and auto-normal models as instances of Markov random fields and
paved the way for their use in diverse applications. Later work included
investigations into the efficacy of nearest neighbour models to accommodate
spatial dependence in the analysis of data from agricultural field trials,
image restoration from noisy data, and texture generation using lattice models.Comment: 26 pages, 14 figures; minor revisions, omission of full bibliograph
A Partial Likelihood for Spatio-temporal Point Processes
Spatio-temporal point process data arise in many fields of application. An intuitively natural way to specify a model for a spatio-temporal point process is through its conditional intensity at location x and time t, given the history of the process up to time t. Typically, this results in an analytically intractable likelihood. Likelihood-based inference therefore relies on Monte Carlo methods which are computationally intensive and require careful tuning to each application. We propose a partial likelihood alternative which is computationally straightforward and can be applied routinely. We apply the method to data from the 2001 foot-and-mouth epidemic in the UK, using a previously published model for the spatio-temporal spread of the disease
Clustering of equine grass sickness cases in the United Kingdom: a study considering the effect of position-dependent reporting on the space-time K-function
Equine grass sickness (EGS) is a largely fatal, pasture-associated dysautonomia. Although the aetiology of this disease is unknown, there is increasing evidence that Clostridium botulinum type C plays an important role in this condition. The disease is widespread in the United Kingdom, with the highest incidence believed to occur in Scotland. EGS also shows strong seasonal
variation (most cases are reported between April and July). Data from histologically confirmed cases of EGS from England and Wales in 1999 and 2000 were collected from UK veterinary diagnostic centres. The data did not represent a complete census of cases, and the proportion of all cases reported to the centres would have varied in space and, independently, in time. We consider the variable reporting of this condition and the appropriateness of the space–time K-function when exploring the spatial-temporal properties of a ‘thinned’ point process. We
conclude that such position-dependent under-reporting of EGS does not invalidate the Monte Carlo test for space–time interaction, and find strong evidence for space–time clustering of EGS cases (P<0.001). This may be attributed to contagious or other spatially and temporally localized processes such as local climate and/or pasture management practices
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