162 research outputs found

    Statistical models for over-dispersion in the frequency of peaks over threshold data for a flow series.

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    In a peaks over threshold analysis of a series of river flows, a sufficiently high threshold is used to extract the peaks of independent flood events. This paper reviews existing, and proposes new, statistical models for both the annual counts of such events and the process of event peak times. The most common existing model for the process of event times is a homogeneous Poisson process. This model is motivated by asymptotic theory. However, empirical evidence suggests that it is not the most appropriate model, since it implies that the mean and variance of the annual counts are the same, whereas the counts appear to be overdispersed, i.e., have a larger variance than mean. This paper describes how the homogeneous Poisson process can be extended to incorporate time variation in the rate at which events occur and so help to account for overdispersion in annual counts through the use of regression and mixed models. The implications of these new models on the implied probability distribution of the annual maxima are also discussed. The models are illustrated using a historical flow series from the River Thames at Kingston

    Reversible jump MCMC for nonparametric drift estimation for diffusion processes

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    In the context of nonparametric Bayesian estimation a Markov chain Monte Carlo algorithm is devised and implemented to sample from the posterior distribution of the drift function of a continuously or discretely observed one-dimensional diffusion. The drift is modeled by a scaled linear combination of basis functions with a Gaussian prior on the coefficients. The scaling parameter is equipped with a partially conjugate prior. The number of basis function in the drift is equipped with a prior distribution as well. For continuous data, a reversible jump Markov chain algorithm enables the exploration of the posterior over models of varying dimension. Subsequently, it is explained how data-augmentation can be used to extend the algorithm to deal with diffusions observed discretely in time. Some examples illustrate that the method can give satisfactory results. In these examples a comparison is made with another existing method as well

    Statistical inference of the mechanisms driving collective cell movement

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    Numerous biological processes, many impacting on human health, rely on collective cell movement. We develop nine candidate models, based on advection-diffusion partial differential equations, to describe various alternative mechanisms that may drive cell movement. The parameters of these models were inferred from one-dimensional projections of laboratory observations of Dictyostelium discoideum cells by sampling from the posterior distribution using the delayed rejection adaptive Metropolis algorithm (DRAM). The best model was selected using the Widely Applicable Information Criterion (WAIC). We conclude that cell movement in our study system was driven both by a self-generated gradient in an attractant that the cells could deplete locally, and by chemical interactions between the cells

    Burglary in London: insights from statistical heterogeneous spatial point processes

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    To obtain operational insights regarding the crime of burglary in London, we consider the estimation of the effects of covariates on the intensity of spatial point patterns. Inspired by localized properties of criminal behaviour, we propose a spatial extension to mixtures of generalized linear models from the mixture modelling literature. The Bayesian model proposed is a finite mixture of Poisson generalized linear models such that each location is probabilistically assigned to one of the groups. Each group is characterized by the regression coefficients, which we subsequently use to interpret the localized effects of the covariates. By using a blocks structure of the study region, our approach enables specifying spatial dependence between nearby locations. We estimate the proposed model by using Markov chain Monte Carlo methods and we provide a Python implementation

    Genome sequencing of the extinct Eurasian wild aurochs, Bos primigenius, illuminates the phylogeography and evolution of cattle

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    Background Domestication of the now-extinct wild aurochs, Bos primigenius, gave rise to the two major domestic extant cattle taxa, B. taurus and B. indicus. While previous genetic studies have shed some light on the evolutionary relationships between European aurochs and modern cattle, important questions remain unanswered, including the phylogenetic status of aurochs, whether gene flow from aurochs into early domestic populations occurred, and which genomic regions were subject to selection processes during and after domestication. Here, we address these questions using whole-genome sequencing data generated from an approximately 6,750-year-old British aurochs bone and genome sequence data from 81 additional cattle plus genome-wide single nucleotide polymorphism data from a diverse panel of 1,225 modern animals. Results Phylogenomic analyses place the aurochs as a distinct outgroup to the domestic B. taurus lineage, supporting the predominant Near Eastern origin of European cattle. Conversely, traditional British and Irish breeds share more genetic variants with this aurochs specimen than other European populations, supporting localized gene flow from aurochs into the ancestors of modern British and Irish cattle, perhaps through purposeful restocking by early herders in Britain. Finally, the functions of genes showing evidence for positive selection in B. taurus are enriched for neurobiology, growth, metabolism and immunobiology, suggesting that these biological processes have been important in the domestication of cattle. Conclusions This work provides important new information regarding the origins and functional evolution of modern cattle, revealing that the interface between early European domestic populations and wild aurochs was significantly more complex than previously thought

    Implementation of Physical Employment Standards for Physically Demanding Occupations

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    Objective: The aim of this paper was to describe an approach to implementing and integrating physical employment standards into an organisational procedure, to ensure the safe and effective supervision of physical fitness of workers in a physically demanding occupation, using a real-world example. Methods: Using previously published cardiorespiratory, muscular strength and endurance physical demands data from UK firefighters, a process to manage all levels of physical capability was developed with industry stakeholders. Results: Performance standards and associated cut-scores relating to acceptable, uncertain, and unacceptable job performance, using a traffic-light style process, were agreed by stakeholders to ensure the safe and effective management of incumbent’s physical fitness. Conclusions: This paper describes the processes involved in implementing a physical capability management procedure, for the administration of routine in-service physical employment standards and tests in the UK Fire & Rescue Service

    Macaque models of human infectious disease.

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    Macaques have served as models for more than 70 human infectious diseases of diverse etiologies, including a multitude of agents-bacteria, viruses, fungi, parasites, prions. The remarkable diversity of human infectious diseases that have been modeled in the macaque includes global, childhood, and tropical diseases as well as newly emergent, sexually transmitted, oncogenic, degenerative neurologic, potential bioterrorism, and miscellaneous other diseases. Historically, macaques played a major role in establishing the etiology of yellow fever, polio, and prion diseases. With rare exceptions (Chagas disease, bartonellosis), all of the infectious diseases in this review are of Old World origin. Perhaps most surprising is the large number of tropical (16), newly emergent (7), and bioterrorism diseases (9) that have been modeled in macaques. Many of these human diseases (e.g., AIDS, hepatitis E, bartonellosis) are a consequence of zoonotic infection. However, infectious agents of certain diseases, including measles and tuberculosis, can sometimes go both ways, and thus several human pathogens are threats to nonhuman primates including macaques. Through experimental studies in macaques, researchers have gained insight into pathogenic mechanisms and novel treatment and vaccine approaches for many human infectious diseases, most notably acquired immunodeficiency syndrome (AIDS), which is caused by infection with human immunodeficiency virus (HIV). Other infectious agents for which macaques have been a uniquely valuable resource for biomedical research, and particularly vaccinology, include influenza virus, paramyxoviruses, flaviviruses, arenaviruses, hepatitis E virus, papillomavirus, smallpox virus, Mycobacteria, Bacillus anthracis, Helicobacter pylori, Yersinia pestis, and Plasmodium species. This review summarizes the extensive past and present research on macaque models of human infectious disease

    An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach

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    Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical modelling and geostatistics. The specification through the covariance function gives an intuitive interpretation of the field properties. On the computational side, GFs are hampered with the big n problem, since the cost of factorizing dense matrices is cubic in the dimension. Although computational power today is at an all time high, this fact seems still to be a computational bottleneck in many applications. Along with GFs, there is the class of Gaussian Markov random fields (GMRFs) which are discretely indexed. The Markov property makes the precision matrix involved sparse, which enables the use of numerical algorithms for sparse matrices, that for fields in R-2 only use the square root of the time required by general algorithms. The specification of a GMRF is through its full conditional distributions but its marginal properties are not transparent in such a parameterization. We show that, using an approximate stochastic weak solution to (linear) stochastic partial differential equations, we can, for some GFs in the Matern class, provide an explicit link, for any triangulation of R-d, between GFs and GMRFs, formulated as a basis function representation. The consequence is that we can take the best from the two worlds and do the modelling by using GFs but do the computations by using GMRFs. Perhaps more importantly, our approach generalizes to other covariance functions generated by SPDEs, including oscillating and non-stationary GFs, as well as GFs on manifolds. We illustrate our approach by analysing global temperature data with a non-stationary model defined on a sphere
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