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Anthony C. Davison
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Professor of Statistics
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office(s):
MAB1423
phone(s): [+41 21 69] 35502,35456
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BIOGRAPHY
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Degrees
BA Mathematics, University of Oxford 1980 (MA 1989)
MSc Statistics, Imperial College London 1981
PhD Statistics, Imperial College London 1984
Appointments
University of Texas at Austin: Assistant Professor 1984-1986.
Imperial College London: Lecturer in Statistics 1986-1989.
University of Oxford: University Lecturer in Statistics, member of the Department of Statistics and Fellow of Lady Margaret Hall 1989-1996.
EPSRC Advanced Research Fellow, 1993-1998
Ecole Polytechnique Fédérale de Lausanne: Professor of Statistics, 1996-
Research interests
Anthony Davison has published on a wide range of topics in statistical theory and methods, and on environmental, biological and financial applications. His current main research interests are statistics of extremes, likelihood asymptotics, bootstrap and other resampling methods, and statistical modelling.
Statistics of extremes concerns rare events such as storms, high winds and tides, extreme pollution episodes, sporting records, and the like. The subject has a long history, but under the impact of engineering and environmental problems has been an area of intense development in the past 15 years. Davison''s PhD work was in this area, in a project joint between the Departments of Mathematics and Mechanical Engineering at Imperial College, with the aim of modelling potential high exposures to radioactivity due to releases from nuclear installations. The key tools developed, joint with Richard Smith, were regression models for exceedances over high thresholds, which generalized earlier work by hydrologists, and formed the basis of some important later developments. This has led to an ongoing interest in extremes, and in particular their application to environmental and financial data. A major current interest is the development of suitable methods for modelling rare spatio-temporal events, particularly but not only in the context of climate change. Davison is principal investigator for a major project on this topic funded by the the CEPF and involving researchers from EPFL, ETHZ, WSL, SSS, and other institutions.
Likelihood asymptotics too have undergone very substantial development since 1980. Key tools here have been saddlepoint and related approximations, which can give remarkably accurate approximate distribution and density functions even for very small sample sizes. These approximations can be used for wide classes of parametric models, but also for certain bootstrap and resampling problems. The literature on these methods can seem arcane, but they are potentially widely applicable, and a book written joint with Nancy Reid and Alessandra Brazzale to promote their use in applications has recently appeared.
Bootstrap methods are now used in many areas of application, where they can provide a researcher with accurate inferences tailor-made to the data available, rather than relying on large-sample or other approximations of doubtful validity. The key idea is to replace analytical calculations of biases, variances, confidence and prediction intervals, and other measures of uncertainty with computer simulation from a suitable statistical model. In a nonparametric situation this model consists of the data themselves, and the simulation simply involves resampling from the existing data, while in a parametric case it involves simulation from a suitable parametric model. There is a wide range of possibilities between these extremes, and the book by Davison and Hinkley explores these for many data examples, with the aim of showing how and when resampling methods succeed and why they can fail.
Davison also has research links with the NCCR Plant Survival at the University of Neuchâtel, for which he leads a group of researchers undertaking statistical and dynamical modelling. The problems studied here are very varied, ranging from methods for the analysis of microarray data, through modelling the behaviour of wasps and moths, to assessment of the effects of grazing by cows in their pastures.
He is Editor of Biometrika (2008-). Other editorial roles have been as Joint Editor of Journal of the Royal Statistical Society, series B (2000-2003), editor of the IMS Lecture Notes – Monograph Series (2007), Associate Editor of Biometrika (1987-1999), and Associate Editor of the Brazilian Journal of Probability and Statistics (1987 – 2006). He has served on several committees of Royal Statistical Society, including its Research Section Committee. He is an elected Fellow of the American Statistical Assocation and the Institute of Mathematical Statistics, an elected member of the International Statistical Institute, and a Chartered Statistician.
In 2009 he was awarded a laurea honoris causa in Statistical Science by the University of Padova.
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MAIN PUBLICATIONS
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A. J. Canty, A. C. Davison, D. V. Hinkley, and V. Ventura.
Bootstrap diagnostics and remedies.
Canadian Journal of Statistics, 34:5-27, 2006.
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Details ]
I. Ricard and A. C. Davison.
Statistical inference for olfactometer data.
Applied Statistics, 56:479-492, 2007.
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A. C. Davison, D. A. S. Fraser, and N. Reid.
Improved likelihood inference for discrete data.
Journal of the Royal Statistical Society series B,
68:495-508, 2006.
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Details ]
V. Chavez-Demoulin, A. C. Davison, and A. J. McNeil.
A point process approach to value-at-risk estimation.
Quantitative Finance, 5(2):227-234, 2005.
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V. Chavez-Demoulin and A. C. Davison.
Generalized additive models for sample extremes.
Applied Statistics, 54:207-222, 2005.
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A. R. Brazzale, A. C. Davison, and N. Reid.
Applied Asymptotics: Case Studies in Small-Sample
Statistics.
Cambridge University Press, Cambridge, 2007.
[ Details |
Link ]
A. C. Davison, Y. Dodge, and N. Wermuth.
Celebrating Statistics: Papers in Honour of Sir David
Cox on the Occasion of his 80th Birthday.
Oxford University Press, Oxford, UK, 2005.
[ Details |
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A. C. Davison.
Statistical Models.
Cambridge University Press, Cambridge, 2003.
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A. C. Davison and D. V. Hinkley.
Bootstrap Methods and Their Application.
Cambridge University Press, Cambridge, 1997.
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D. Bhowmick, A. C. Davison, D. R. Goldstein, and Y. Ruffieux.
A Laplace mixture model for the identification of differential
expression in microarrays.
Biostatistics, 7:630-641, 2006.
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A. C. Davison and N. I. Ramesh.
Local likelihood smoothing of sample extremes.
Journal of the Royal Statistical Society series B,
62:191-208, 2000.
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A. C. Davison.
Biometrika centenary: Theory and general methodology.
Biometrika, 88(1):13-52, 2001.
Reprinted in Biometrika: One Hundred Years, edited by
D. M. Titterington and D. R. Cox. Oxford University Press, [11]-[50].
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A. Ancey, A.C. Davison, T. Böhm, M. Jodeau, and P. Frey.
Entrainment and motion of coarse particles in a shallow water stream
down a steep slope.
Journal of Fluid Mechanics, 595:83-114, 2008.
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T. Clerc, A. C. Davison, and L.-F. Bersier.
Stochastic modelling of prey depletion processes.
Journal of Theoretical Biology, 259:523-532, 2009.
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Mária Süveges and Anthony C. Davison.
Model misspecification in peaks over threshold analysis.
The Annals of Applied Statistics, 4(1):203-221, 2010.
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Full Text ]
A. R. Brazzale and A. C. Davison.
Accurate parametric inference for small samples.
Statistical Science, 23(4):465-484, 2008.
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Full Professor 
