Papers by Martin Hazelton
Journal of Vertebrate Paleontology, Jan 1, 2007
ABSTRACT Compagopiscis croucheri and Gogopiscis gracilis (Gardiner and Miles, 1994) were collecte... more ABSTRACT Compagopiscis croucheri and Gogopiscis gracilis (Gardiner and Miles, 1994) were collected in high numbers and in different size classes from the Upper Devonian Gogo Formation, Western Australia. This material has provided the basis for an investigation of ontogentic and phenotypic variation of characters and their phylogenetic implications. We used the Principal Component Analysis to examine if variation between specimens assigned to C. croucheri and G. gracilis could be attributed to random variation. In our ...
Molecular biology and evolution, Jan 12, 2015
Marriage rules, the community prescriptions that dictate who an individual can or cannot marry, a... more Marriage rules, the community prescriptions that dictate who an individual can or cannot marry, are extremely diverse and universally present in traditional societies. A major focus of research in the early decades of modern anthropology, marriage rules impose social and economic forces that help structure societies and forge connections between them. However, in those early anthropological studies, the biological benefits or disadvantages of marriage rules could not be determined. We revisit this question by applying a novel simulation framework and genome-wide data to explore the effects of Asymmetric Prescriptive Alliance, an elaborate set of marriage rules that has been a focus of research for many anthropologists. Simulations show that strict adherence to these marriage rules reduces genetic diversity on the autosomes, X chromosome and mitochondrial DNA, but relaxed compliance produces genetic diversity similar to random mating. Genome-wide data from the Indonesian community of...
EURO Journal on Transportation and Logistics, 2013
ABSTRACT Real-life systems are known to exhibit considerable day-to-day variability. A better und... more ABSTRACT Real-life systems are known to exhibit considerable day-to-day variability. A better understanding of such variability has increasing policy-relevance in the context of network reliability assessment and the design of intelligent transport systems. Conventional equilibrium models are ill-suited, because deterministic models such as these do not account for any kind of variability. At best, these types of models are restricted to finding a steady state of the mean flow patterns, they cannot capture the variance in flows as well. A more suitable alternative are stochastic day-to-day dynamic models studied by Cascetta in Trans Res 23:1–17, (1989). These types of traffic assignment models represent the traffic flows via a Markov process, where the current route flows are modelled as a function of previous traffic conditions. Day-to-day dynamic models differ from equilibrium models in that day-to-day changes in the system are modelled dependent on the time and thus allow for a far wider representation of traveller behaviour. However, to some degree they still suffer from some of the limitations of equilibrium analyses, in that while they permit variation they are still wedded to the concept of ‘stationarity’. In this paper, we show how these Markovian day-to-day dynamic traffic assignment models can be extended by replacing a subset of the fixed parameters in the Markov model with random processes. The resulting models are analogous to Cox process models. They are conditionally non-stationary given any realization of the parameter processes. We present numerical examples that demonstrate that this new class of doubly stochastic day-to-day traffic assignment models can indeed reproduce features such as the heteroscedasticity of traffic flows observed in real-life settings.
Transportation Research Part B: Methodological, 2014
Spatial and spatio-temporal epidemiology, 2014
The widespread availability of computer hardware and software for recording and storing disease e... more The widespread availability of computer hardware and software for recording and storing disease event information means that, in theory, we have the necessary information to carry out detailed analyses of factors influencing the spatial distribution of disease in animal populations. However, the reliability of such analyses depends on data quality, with anomalous records having the potential to introduce significant bias and lead to inappropriate decision making. In this paper we promote the use of exceedance probabilities as a tool for detecting anomalies when applying hierarchical spatio-temporal models to animal health data. We illustrate this methodology through a case study data on outbreaks of foot-and-mouth disease (FMD) in Viet Nam for the period 2006-2008. A flexible binomial logistic regression was employed to model the number of FMD infected communes within each province of the country. Standard analyses of the residuals from this model failed to identify problems, but ex...
Investigative ophthalmology & visual science, 2014
Retinal vein pulsation properties are altered by glaucoma, intracranial pressure (ICP) changes, a... more Retinal vein pulsation properties are altered by glaucoma, intracranial pressure (ICP) changes, and retinal venous occlusion, but measurements are limited to threshold measures or manual observation from video frames. We developed an objective retinal vessel pulsation measurement technique, assessed its repeatability, and used it to determine the phase relations between retinal arteries and veins. Twenty-three eyes of 20 glaucoma patients had video photograph recordings from their optic nerve and peripapillary retina. A modified photoplethysmographic system using video recordings taken through an ophthalmodynamometer and timed to the cardiac cycle was used. Aligned video frames of vessel segments were analyzed for blood column light absorbance, and waveform analysis was applied. Coefficient of variation (COV) was calculated from data series using recordings taken within ±1 unit ophthalmodynamometric force of each other. The time in cardiac cycles and seconds of the peak (dilation) a...
Ophthalmology, 2004
To determine whether changes in central retinal vein pulsation characteristics occur in glaucoma,... more To determine whether changes in central retinal vein pulsation characteristics occur in glaucoma, and how these are related to indices of glaucoma severity. A large, consecutive, prospective, case-controlled study. Ninety-four consecutive glaucoma patients and 105 glaucoma suspects seen in a tertiary referral clinic were examined. Forty-one age-matched normal subjects also were examined. The presence or absence of spontaneous venous pulsation was observed in these 3 groups. The ophthalmodynamometric force (ODF) required to induce venous pulsation at the optic disc was measured in those without spontaneous pulsation. Optic disc photographs were obtained and visual field testing was performed for all subjects. The prevalence of spontaneous venous pulsation between these 3 groups was compared. The relationship between ODF and visual field mean deviation, neuroretinal rim area, age, intraocular pressure (IOP), gender, and diagnosis of glaucoma was investigated using linear mixed models fitted by Gibb's sampling. Significantly fewer (chi-square, 27.7; P<0.001) glaucoma patients (54%) were observed to have spontaneous venous pulsation than suspects (75%) or normals (98%). A worse visual field mean deviation was shown to be the most significant predictor of a higher ODF (P<0.000), with younger age (P<0.000) also predictive of a higher ODF. A strong relationship between ODF and mean deviation was found in the glaucoma patients (r = 0.59; n = 52; P<0.001). Spontaneous venous pulsation is less common in glaucoma. The ODF required to induce venous pulsation is increased in glaucoma, and this ODF is greater in those with more severe field loss.
Veterinary Research, 2008
We describe the spatial epidemiological features of the 6.8 million meat-juice serological tests ... more We describe the spatial epidemiological features of the 6.8 million meat-juice serological tests that were conducted between 1995 and 2004 as part of the Danish swine Salmonella control programme. We investigated pig and farm density using edge-corrected kernel estimations. Pigs were aggregated at the county level to assess county-level risk, and then we investigated farm-level risk by giving farms a case or non-case label using a cut-off of 40% of pigs positive. Conditional probability surfaces, correcting for the underlying population at risk, were produced for each year of the study period using a novel kernel estimator with a spatially adaptive smoothing bandwidth. This approach improves on previous methods by allowing focussed estimation of risk in areas of high population density while maintaining stable estimates in regions where the data are sparse. Two spatial trends in the conditional probability of a farm being a case were evident: (1) over the whole country, with the highest risk in the west compared to the east; and (2) on the Jutland peninsula with the highest risk in the north and south. At the farm-level a consistent area of risk was the south-west of Jutland. Case farms tended to aggregate indicating spatial dependency in the data. We found no association between pig or farm density and Salmonella risk. We generated hypotheses for this spatial pattern of risk and we conclude that this spatial pattern should be considered in the development of surveillance strategies and as a basis for further, more detailed analyses of the data.
PloS one, 2015
Retinal venous pulsation detection is a subjective sign, which varies in elevated intracranial pr... more Retinal venous pulsation detection is a subjective sign, which varies in elevated intracranial pressure, venous obstruction and glaucoma. To date no method can objectively measure and identify pulsating regions. Using high resolution video-recordings of the optic disk and retina we measured fluctuating light absorption by haemoglobin during pulsation. Pulsation amplitude was calculated from all regions of the retinal image video-frames in a raster pattern. Segmented retinal images were formed by objectively selecting regions with amplitudes above a range of threshold values. These were compared to two observers manually drawing an outline of the pulsating areas while viewing video-clips in order to generate receiver operator characteristics. 216,515 image segments were analysed from 26 eyes in 18 research participants. Using data from each eye, the median area under the receiver operator curve (AU-ROC) was 0.95. With all data analysed together the AU-ROC was 0.89. We defined the ide...
Transportmetrica A: Transport Science, 2013
Veterinary Research, 2008
We describe the spatial epidemiological features of the 6.8 million meat-juice serological tests ... more We describe the spatial epidemiological features of the 6.8 million meat-juice serological tests that were conducted between 1995 and 2004 as part of the Danish swine Salmonella control programme. We investigated pig and farm density using edge-corrected kernel estimations. Pigs were aggregated at the county level to assess county-level risk, and then we investigated farm-level risk by giving farms a case or non-case label using a cut-off of 40% of pigs positive. Conditional probability surfaces, correcting for the underlying population at risk, were produced for each year of the study period using a novel kernel estimator with a spatially adaptive smoothing bandwidth. This approach improves on previous methods by allowing focussed estimation of risk in areas of high population density while maintaining stable estimates in regions where the data are sparse. Two spatial trends in the conditional probability of a farm being a case were evident: (1) over the whole country, with the highest risk in the west compared to the east; and (2) on the Jutland peninsula with the highest risk in the north and south. At the farm-level a consistent area of risk was the south-west of Jutland. Case farms tended to aggregate indicating spatial dependency in the data. We found no association between pig or farm density and Salmonella risk. We generated hypotheses for this spatial pattern of risk and we conclude that this spatial pattern should be considered in the development of surveillance strategies and as a basis for further, more detailed analyses of the data.
Transportation Research Part B: Methodological, 2013
ABSTRACT There is significant current interest in the development of models to describe the day-t... more ABSTRACT There is significant current interest in the development of models to describe the day-to-day evolution of traffic flows over a network. We consider the problem of statistical inference for such models based on daily observations of traffic counts on a subset of network links. Like other inference problems for network-based models, the critical difficulty lies in the underdetermined nature of the linear system of equations that relates link flows to the latent path flows. In particular, Bayesian inference implemented using Markov chain Monte Carlo methods requires that we sample from the set of route flows consistent with the observed link flows, but enumeration of this set is usually computationally infeasible.We show how two existing conditional route flow samplers can be adapted and extended for use with day-to-day dynamic traffic. The first sampler employs an iterative route-by-route acceptance–rejection algorithm for path flows, while the second employs a simple Markov model for traveller behaviour to generate candidate entire route flow patterns when the network has a tree structure. We illustrate the application of these methods for estimation of parameters that describe traveller behaviour based on daily link count data alone.
Statistics in Medicine, 2005
The semi-parametric regression achieved via penalized spline smoothing can be expressed in a line... more The semi-parametric regression achieved via penalized spline smoothing can be expressed in a linear mixed models framework. This allows such models to be ÿtted using standard mixed models software routines with which many biostatisticians are familiar. Moreover, the analysis of complex correlated data structures that are a hallmark of biostatistics, and which are typically analysed using mixed models, can now incorporate directly smoothing of the relationship between an outcome and covariates. In this paper we provide an introduction to both linear mixed models and penalized spline smoothing, and describe the connection between the two. This is illustrated with three examples, the ÿrst using birth data from the U.K., the second relating mammographic density to age in a study of female twin-pairs and the third modelling the relationship between age and bronchial hyperresponsiveness in families. The models are ÿtted in R (a clone of S-plus) and using Markov chain Monte Carlo (MCMC) implemented in the package WinBUGS.
Statistics and Computing, 2009
Nonparametric density estimation in the presence of measurement error is considered. The usual ke... more Nonparametric density estimation in the presence of measurement error is considered. The usual kernel deconvolution estimator seeks to account for the contamination in the data by employing a modified kernel. In this paper a new approach based on a weighted kernel density estimator is proposed. Theoretical motivation is provided by the existence of a weight vector that perfectly counteracts the bias in density estimation without generating an excessive increase in variance. In practice a data driven method of weight selection is required. Our strategy is to minimize the discrepancy between a standard kernel estimate from the contaminated data on the one hand, and the convolution of the weighted deconvolution estimate with the measurement error density on the other hand. We consider a direct implementation of this approach, in which the weights are optimized subject to sum and non-negativity constraints, and a regularized version in which the objective function includes a ridge-type penalty. Numerical tests suggest that the weighted kernel estimation can lead to tangible improvements in performance over the usual kernel deconvolution estimator. Furthermore, weighted kernel estimates are free from the problem of neg-ative estimation in the tails that can occur when using modified kernels. The weighted kernel approach generalizes to the case of multivariate deconvolution density estimation in a very straightforward manner.
Spatial and Spatio-temporal Epidemiology, 2014
The spatial relative risk function is defined as the ratio of densities describing respectively t... more The spatial relative risk function is defined as the ratio of densities describing respectively the spatial distribution of cases and controls. It has proven to be an effective tool for visualizing spatial variation in risk in many epidemiological applications over the past 20 years. We discuss the generalization of this function to spatio-temporal case-control data, and also to situations where there are covariates available that may affect the spatial patterns of disease. We examine estimation of the generalized relative risk functions using kernel smoothing, including asymptotic theory and data-driven bandwidth selection. We also consider construction of tolerance contours. Our methods are illustrated on spatio-temporal data describing the 2001 outbreak of foot-and-mouth disease in the United Kingdom, with farm size as a covariate.
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Papers by Martin Hazelton