Anthony Christopher Davison

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We outline how modern likelihood theory, which provides essentially exact inferences in a variety of parametric statistical problems, may routinely be applied in practice. Although the likelihood procedures are based on analytical asymptotic approximations ...

Near-surface wind is difficult to estimate using global numerical weather and climate models, because airflow is strongly modified by underlying topography, especially that of a country such as Switzerland. In this article, we use a statistical approach ba ...

In this study ideas from extreme value theory are for the first time applied in the field of stratospheric ozone research, because statistical analysis showed that previously used concepts assuming a Gaussian distribution (e.g. fixed deviations from mean v ...

The modeling of multivariate extremes has received increasing recent attention because of its importance in risk assessment. In classical statistics of extremes, the joint distribution of two or more extremes has a nonparametric form, subject to moment con ...

The Brown-Resnick max-stable process has proven to be well suited for modeling extremes of complex environmental processes, but in many applications its likelihood function is intractable and inference must be based on a composite likelihood, thereby preve ...

Different dependence scenarios can arise in multivariate extremes, entailing careful selection of an appropriate class of models. In bivariate extremes, the variables are either asymptotically dependent or are asymptotically independent. Most available sta ...

Bootstrap methods are powerful and widely useful simulation procedures, whose applications include assessing the uncertainty of a statistical estimator, constructing a confidence interval, and computing the significance probability associated with a hypothesis test.

Two-component mixture distributions with one component a point mass and the other a continuous density may be used as priors for Bayesian inference when sparse representation of an underlying signal is required. We show how saddlepoint approximation in suc ...

Meta-analysis of microarray studies to produce an overall gene list is relatively straightforward when complete data are available. When some studies lack information, for example, having only a ranked list of genes instead of complete primary data, it is ...

The paper contains description of the implementation of C code for tree representation of Markov Chain Monte Carlo(MCMC) clustering. The aim of the code is to produce results which helps in visual representation of the most frequent pattern, its agglomerations and divisions and thus helps in constructing a tree also called as dendrogram. It illustrates the mergers or divisions which have been made at successive levels. The paper explains codes for various code functions used for finding the most frequent pa

The areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood protection. This article reviews recent progress in the ...

Universal inference enables the construction of confidence intervals and tests without regularity conditions by splitting the data into two parts and appealing to Markov's inequality. Previous investigations have shown that the cost of this generality is a ...

Severe thunderstorms cause substantial economic and human losses in the United States. Simultaneous high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are favorable to severe weather, and both they and the composi ...

Saddlepoint techniques have been used successfully in many applications, owing to the high accuracy with which they can approximate intractable densities and tail probabilities. This article concerns their use for the estimation of high-order integer-value ...

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 likelihood. The result is an empirical Bayes thresholding procedure whose results improve on those of wavethresh in terms of mean square error. We apply the approach to data from the SensorScope environmental modelling system, and briefly discuss issues that aris

The spatial modelling of extreme snow is important for adequate risk management in Alpine and high altitude countries. A natural approach to such modelling is through the theory of max-stable processes, an infinite-dimensional extension of multivariate ext ...

We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, ...

Statistical diagnostics are used to help data analysts detect and find the source of any problems that may have arisen with the application of statistical procedures, and if possible to suggest remedies. Often they have two elements: a graph constructed to highlight potential difficulties and a numerical test statistic intended to help gauge their significance. Regression involves studying the dependence of one or a few variables on others and is perhaps the most widespread statistical activity. Consequentl

Many quantitative genetic statistics are functions of variance components, for which a large number of replicates is needed for precise estimates and reliable measures of uncertainty, on which sound interpretation depends. Moreover, in large experiments th ...

Anthony Christopher Davison

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Accurate parametric inference for small samples

Downscaling of Historical Wind Fields over Switzerland Using Generative Adversarial Networks

Extreme events in total ozone over Arosa---Part 1: Application of extreme value theory

Spectral Density Ratio Models for Multivariate Extremes

Bayesian Inference For The Brown-Resnick Process, With An Application To Extreme Low Temperatures

Modelling across extremal dependence classes

Bootstrap Inference

Saddlepoint approximation for mixture models

Meta-analysis of incomplete microarray studies

Some Basic Functions for Tree Representations of Bayesian Markov Chain Monte Carlo Clustering

Statistical Modeling of Spatial Extremes

'The life of man, solitary, poore, nasty, brutish, and short': Discussion of the paper by Rootzen and Zholud

A note on universal inference

Space-Time Extremes of Severe U.S. Thunderstorm Environments

Likelihood Estimation for the INAR(p) Model by Saddlepoint Approximation

Hierarchical wavelet modelling of environmental sensor data

Spatial modelling of extreme snow depth

Overcoming Multi-model Forgetting

Regression Diagnostics

Reliable confidence intervals in quantitative genetics: Narrow-sense heritability