Anthony Christopher Davison

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Motivated by the need to smooth and to summarize multiple simultaneous time series arising from networks of environmental monitors, we propose a hierarchical wavelet model for which estimation of hyperparameters can be performed by marginal maximum likelih ...

Choosing the plant on which to lay their eggs is the last act of care that most female herbivorous insects bestow upon their offspring. These decisions play a pivotal role in insect–plant interactions, placing host preference under strong selection and ...

Diabetes is characterized by hyperglycemia that can result from the loss of pancreatic insulin secreting β-cells in the islets of Langerhans. We analyzed ex vivo the entire gastric and duodenal lobes of a murine pancreas using extended-focus Optical Cohere ...

Both marginal and dependence features must be described when modelling the extremes of a stationary time series. There are standard approaches to marginal modelling, but long-and short-range dependence of extremes may both appear. In applications, an assum ...

Clustering and classification of replicated data is often performed using classical techniques that inappropriately treat the data as unreplicated, or by complex modern ones that are computationally demanding. In this paper, we introduce a simple approach ...

Despite its importance for insurance, there is almost no literature on statistical hail damage modeling. Statistical models for hailstorms exist, though they are generally not open-source, but no study appears to have developed a stochastic hail impact fun ...

Statistics of spatial extremes is developing very rapidly, owing to the demands of applications in the environmental sciences and the insurance and risk industries. This entry sketches the main ideas, based on classical extreme-value statistics. The two main threads of work are the fitting of max-stable models to spatial extremes, and the use of latent variable models, the latter typically in a Bayesian context.

Expectiles define the only law-invariant, coherent and elicitable risk measure apart from the expectation. The popularity of expectile-based risk measures is steadily growing and their properties have been studied for independent data, but further results ...

Olfactometer experiments are used to study the responses of arthropods to potential attractants, for purposes such as understanding natural defenses of plants against their herbivores. Such experiments typically lead to multivariate data consisting of smal ...

Particle physics experiments such as those run in the Large Hadron Collider result in huge quantities of data, which are boiled down to a few numbers from which it is hoped that a signal will be detected. We discuss a simple probability model for this and ...

We propose an approach to spatial modeling of extreme rainfall, based on max-stable processes fitted using partial duration series and a censored threshold likelihood function. The resulting models are coherent with classical extreme-value theory and allow ...

We discuss variance estimation by resampling in surveys in which data are missing. We derive a formula for jackknife linearization in the case of calibrated estimation with deterministic regression imputation, and compare the resulting variance estimates w ...

Max-stable processes are increasingly widely used for modelling complex extreme events, but existing fitting methods are computationally demanding, limiting applications to a few dozen variables. r-Pareto processes are mathematically simpler and have the p ...

The modern theory of likelihood inference provides improved inferences in many parametric models, with little more effort than is required for application of standard first-order theory. We outline the relevant computations, and illustrate the calculations ...

The R package bclust is useful for clustering high-dimensional continuous data. The package uses a parametric spike-and-slab Bayesian model to downweight the effect of noise variables and to quantify the importance of each variable in agglomerative cluster ...

Olfactometer experiments are used to determine the effect of odours on the behaviour of organisms such as insects or nematodes, and typically result in data comprising many groups of small overdispersed counts. We develop a non-homogeneous Markov chain mod ...

We describe a failure of standard extremal models to account for a catastrophic rainfall event in the coastal regions of Venezuela on 14-16 December 1999, due both to inaccurate tail modelling and to an inadequate treatment of clusters of rare events. We i ...

Anthony Christopher Davison

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Hierarchical wavelet modelling of environmental sensor data

Conserved oviposition preferences in alpine leaf beetle populations despite host shifts and isolation

Diabetes imaging — quantitative assessment of islets of Langerhans distribution in murine pancreas using extended-focus optical coherence microscopy

Bayesian uncertainty management in temporal dependence of extremes

A simple model-based approach to variable selection in classification and clustering

BAYESIAN MODELING OF INSURANCE CLAIMS FOR HAIL DAMAGE

Extremes: spatial parametric modeling

Tail Risk Inference via Expectiles in Heavy-Tailed Time Series

Special Issue: "Data Science versus Classical Inference: Prediction, Estimation, and Attribution", honouring Prof. Brad Efron's International Prize in Statistics in 2019 Discussion

Comparison of Models for Olfactometer Data

The Banff challenge: Statistical detection of a noisy signal

Threshold modeling of extreme spatial rainfall

Resamping variance estimation in surveys with missing data

High-dimensional peaks-over-threshold inference

Three Examples of Accurate Likelihood Inference

High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust

Anthony C. Davison and Raphael de Fondeville's contribution to the Discussion of 'Inference for extreme spatial temperature events in a changing climate with application to Ireland' by Healy et al

Statistical inference for olfactometer data

A case study of a "Dragon-King": The 1999 Venezuelan catastrophe

Improved likelihood inference for discrete data