Scientific/Program Committee

  • Boris Buchmann (Australian National University, ANU)
  • Aurore Delaigle (Melbourne)
  • Yuguang Fan (ANU)
  • Ana Ferriera (Lisbon, Portugal)
  • Bronwyn Loong (ANU)
  • Ross Maller (ANU)
  • Luke Prendergast (Latrobe)
  • Dale Roberts (ANU)
  • Steven Roberts (ANU)
  • Alan Welsh (ANU)


Distinguished Invited Speakers, Background

Professor Aurore Delaigle
Department of Mathematics and Statistics
Faculty of Science
The University of Melbourne

Aurore obtained her PhD from the University of Louvain and currently holds an ARC Queen Elizabeth II Fellowship at the University of Melbourne. . Other fellowhips and awards held include an IMS Fellowship, the Moran Medal, the Hellman Fellowship, and a BAEF Fellowship.  She is an Associate Editor for the Annals of Statistics, JASA, JCGS, JRSSB, the Australian and New Zealand Journal of Statistics, Statistica Sinica, is on the editorial board of the Springer Series Statistique et probabilités appliquées, and was Associate Editor for the Journal of Nonparametric Statistics and the Journal of the Korean Statistical Society.  Her reserach emphasis recently has been on the classification of functional data, joint with Peter Hall and others.  Some recent publications include:

Bennett, M., Melatos, A., Delaigle, A. and Hall, P. (2013). Reanalysis of F-Statistic Gravitational-Wave Searches with the Higher criticism Statistic. The Astrophysical Journal , 766.

Delaigle, A. and Hall, P. (2013). Classification using censored functional data. JASA, 108, 1269-1283.

Carroll, R.J., Delaigle, A., Hall, P. (2012). Deconvolution When Classifying Noisy Data Involving Transformations. JASA, 107, 1166-1177.

Delaigle, A., Hall, P. and Bathia, N. (2012). Componentwise classification and clustering of functional data. Biometrika, 99, 299-313

Delaigle, A. and Hall, P. (2012). Nonparametric regression with homogeneous group testing data. Annals of Statistics, 40, 131-158.


Professor Debbie Dupuis
Department of Management Sciences
HEC Montreal
3000, chemin de la Côte-Sainte-Catherine
Montréal (Québec)
Canada H3T 2A7

Debbie is currently Professor, Department of Management Sciences, HEC Montreal. She has a background in engineering, having obtained her PhD in Statistics from the University of New Brunswick, and has held positions in Departments of Statistics in several Canadian Universities.  Her main research interests are in robustness and extreme values, with applications in areas ranging from temperature modelling to futures derivatives and actuarial studies.  Recent publications include:

Dupuis, D.J and M.-P. Victoria-Feser (2013). Robust VIF Regression with Application to Variable Selection in Large Datasets. The Annals of Applied Statistics, 7, 1, 319-341.

Tsai. Y, D.J. Dupuis, and D.J. Murdoch (2013). A Robust Test for Asymptotic Independence of Bivariate Extremes. Statistics, 47, 1, 172-183.

Dupuis, D.J. (2012). Modeling waves of extreme temperature: The changing tails of four cities. Journal of the American Statistical Association, 107, 24-39.

Tsai, Y., D.J. Murdoch and D.J. Dupuis (2011). Influence Measures and Robust Estimators of Dependence in Multivariate Extremes. Extremes, 14, 343-363.

Dupuis, D.J. and M.-P. Victoria-Feser (2011). Fast Robust Model Selection in Large Datasets. Journal of the American Statistical Association, 106, 203-212.

Dupuis, D.J. (2011). Forecasting Temperature to Price CME Temperature Derivatives. International Journal of Forecasting, 27, 602-618.

Dupuis, D.J., E. Jacquier, N. Papageorgiou and B. Rémillard (2009). Empirical Evidence on the Dependence of Credit Default Swaps and Equity Prices, Journal of Futures Markets, 29, 8, p. 695-712.

Berrada, T., D.J. Dupuis, E. Jacquier, N. Papageorgiou and B. Rémillard (2006). Credit Migration and Basket Derivatives Pricing with Copulas, Journal of Computational Finance, 10, 1, p. 43-68.

Dupuis, D.J. and B.L. Jones (2006). Analysis of Multivariate Extreme Values with Actuarial Applications,  North American Actuarial Journal, 10, 4, p. 1-27. 


Professor Peter Hall 
Department of Mathematics and Statistics
Faculty of Science
The University of Melbourne

Peter Hall needs little introduction to mathematicians and statisticians in Australia or indeed world-wide. His contributions to Probability and Statistics theory and practice,  both, are immense. His MathScinet entry lists 591 publications (papers, and a number of books) going back to 1976, covering a wide variety of subjects including many relevant to the themes of the conference.  The following paper is particularly relevant :  

Delaigle, A., Hall, P. and Jin, J. (2011). Robustness and accuracy of methods for high dimensional data analysis based on Student's t-statistic. J. Roy. Statist. Soc. Ser. B, 73(3), 283-301. 


Prof. Manuel Febrero-Bande
Departamento de Estatística e I.O. 
Facultad de Matemáticas 
Universidad de Santiago de Compostela 
Campus Vida. 15782 Santiago de Compostela. Spain

Dr Febrero obtained his PhD from the Universidad de Santiago de Compostela in 1990.  He is the author of a variety of papers including aspects of generalized additive models with special reference to functional models and data, empirical processes, influence and outliers, etc. Applications are to times series, ozone data, financial data, etc. Recent publications include:

Febrero-Bande, M. and Oviedo, M (2012). "Statistical computing in functional data analysis: the R package fda.usc". Journal of Statistical Software 51 (4) , 1-28 .

Monsalve-Cobis, Abelardo E.; González-Manteiga, W. and Febrero-Bande, M. (2011). "Goodness of test for interest rate models: An approach based on empirical processes". Computational Statistics & Data Analysis 55 (12), 3073–3092. Elsevier Science Bv . 

Matías, J.M.; Febrero-Bande, M.; González-Manteiga, W. and Reboredo, J.C. (2010). "Boosting GARCH and neural networks for the prediction of heteroskedastic time series". Mathematical and Computer Modelling 51, 256-271. Pergamon-Elsevier Science Ltd

Febrero-Bande, M.; Galeano, P. and González-Manteiga, W. (2010). "Measures of influence for the functional linear model with scalar response". Journal of Multivariate Analysis 101, 327-339. Elsevier Inc .


Professor Ana Maria Santos Ferreira Gorjão Henriques
Prof. Auxiliar
CEAUL (Center of Statistics and Applications)
Departamento de Matemática
University of Lisbon

Prof. Ferreira has an outstanding record in the theory and application of extreme value methods. She is author, joint with Laurens De Haan, of the book: Extreme Value Theory: An Introduction , and since then has pursued a vigorous career in the area. Her publications range from highly technical to very applied: see for example her 1997 paper: Extreme Sea Levels in Venice. In: Extreme Value Analysis with XTREMES, R.-D. Reiss and M. Thomas (Editors), (pp. 233-240) Birkhäuser Verlag, Basel.  Prof. Ferreira obtained the following degrees:  in 1993, Licenciatura em Matemática Aplicada e Computação, IST-UTL; in 1997, Mestrado em Probabilidades e Estatística, FCUL-UL; in 2002, Doutoramento em Probabilidades e Estatística, Tilburg University e EURANDOM (Holland).

Extreme Value Theory Haan, Laurens, Ferreira, Ana (417 pp.) Springer, Boston,  offers a careful, coherent exposition of the subject starting from the probabilistic and mathematical foundations and proceeding to the statistical theory. The book covers both the classical one-dimensional case as well as finite- and infinite-dimensional settings. All the main topics at the heart of the subject are introduced in a systematic fashion so that in the final chapter even the most recent developments in the theory can be understood. The treatment is geared toward applications. The presentation concentrates on the probabilistic and statistical aspects of extreme values such as limiting results, domains of attraction and development of estimators without emphasizing related topics such as point processes, empirical distribution functions and Brownian motion. An appendix on regular variation has been added since some required results in that area are not available in book form. The usefulness of the statistical theory is shown by treating several case studies in detail.  The book is a thorough, accessible, self-contained, graduate level treatment of modern extreme value theory and some of its applications. It is aimed at graduate students and researchers and requires only maturity in mathematics and statistics.


Dr Luke Prendergast
Faculty of Science, Technology and Engineering
School of Engineering and Mathematical Sciences
Department of Mathematics and Statistics

Luke is currently Lecturer, Department of Mathematics and Statistics, La Trobe University, from where he obtained his degree in 2006. Since then he has pursued a vigorous career publsihing many papers ranging from the technical to the applied. Luke's research areas include robust statistics, dimension reduction and visualization of high dimensional data, meta analysis (in particular meta regression) and statistical analysis for weight loss studies.  He is currently supervising research students in influence diagnostics for high dimensional data, improvements to dimension reduction methods and meta regression.  Luke also collaborates with the Proietto Research Group, in particular with respect to analysis of weight loss data. Recent papers include:

Prendergast, L. A. and Sheather, S. J. (2013), On sensitivity of inverse response plot estimation and the benefits of a robust estimation approach, Scandinavian Journal of Statistics, Vol. 40, pages 219-237.

Malloy, M. , Prendergast, L. A. and Staudte, R. G. (2013), Transforming the Model-T: Random effects meta-analysis with stable weights, Statistics in Medicine, Vol. 32, pages 1842-1864.

 Baker, S. T., Strauss, B. J., Prendergast, L. A., Panagiotopoulos, S., Thomas, G. E., Vu, T., Proietto, J. and Jerums, G. (2012), Estimating dual-energy X-ray absorptiometry-derived total body skeletal muscle mass using single-slice abdominal magnetic resonance imaging in obese subjects with and without diabetes: a pilot study, European Journal of Clinical Nutrition, Vol. 66, pages 628-632.

Baker, S. T., Jerums, G., Prendergast, L. A., Panagiotopoulos, S., Strauss, B. J. and Proietto, J. (2012), Less fat reduction per unit weight loss in type 2 diabetic compared with nondiabetic obese individuals completing a very-low-calorie diet program, Metabolism: Clinical and Experimental, Vol. 61, Pages 873-882.

 Shaker, A. J. and Prendergast, L. A. (2011), Iterative application of dimension reduction methods, Electronic Journal of Statistics, Vol. 5, pages 1471-1494.


Prof. Elvezio Ronchetti
Research Center for Statistics and Department of Economics
University of Geneva
Blv. Pont d'Arve 40
CH-1211 Geneva

Prof. Elvezio Ronchetti obtained his degree in statistics from the ETH Zurich, under the supervision of F.R. Hampel. He was a Postdoctoral Fellow at Dalhousie University in 1983, and subsequently held positions in the Department of Statistics, Princeton University and the University of Lugano (Switzerland).  His research interests are in the theory of robust statistics and the application of robust procedures to statistics, econometrics, and finance. Other areas include asymptotic expansions, saddlepoint techniques, exploratory data analysis and statistical computing.  He is coauthor with P. Huber of the foundational book Robust Statistics (Wiley, New York, 2nd edition, 2009), and coauthor with F. Hampel, P. Rousseeuw and W. Stahel of the book Robust Statistics: The Approach Based on Influence Functions (Wiley, New York, 2005). He has written a number of other books and many papers in the area of robust statistics.


Professor Matias Salibian-Barrera
Department of Statistics
University of British Columbia
3182 Earth Sciences Building
2207 Main Mall
Vancouver, BC, Canada V6T 1Z4

Matias is currently Associate Professor, Department of Statistics, University of British Columbia.  His research is in robustness and bootstrapping where he is an active contributer. Recent publications include:

 Christmann, A., Salibian-Barrera, M. and van Aelst, S. (2013) Qualitative Robustness of Bootstrap Approximations for Kernel Based Methods. Chapter 16 of Robustness and Complex Data Structures, C. Becker, S. Kuhnt, and R. Fried, Eds. Springer, Heidelberg, New York. 

Kondo, Y., Salibian-Barrera, M. and Zamar, R. H. (2012) A robust and sparse K-means clustering algorithm. arXiv:1201.6082v1.

Azadeh, A. and Salibian-Barrera, M. (2012). An outlier-robust fit for Generalized Additive Models with applications to disease outbreak detection. Journal of the American Statistical Association. 106(494), 719-731.

Tharmaratnam, K., Claeskens, G., Croux, C. and Salibian-Barrera, M. (2010). S-estimation for penalised regression splines. Journal of Computational and Graphical Statistics, 19(3), 609-625. 


Updated:  26 April 2018/Responsible Officer:  Director/Page Contact:  School Manager