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Sponsored by
2015 CRoNoS Winter Course on
Robust methods and multivariate extremes
Dates: 9-11 December 2015.
Venue: Senate Room at Senate House on Wednesday and Thrusday and CLO B01 at CLORE Management Building on Friday, UK.
Link with tutorials: Sessions 4 and 5 of each module will constitute the tutorials of the joint CFE-CMStatistics conference. Participants to the conference can register separately for the tutorials and for Sessions 1-3.

PhD students and Early Career Investigators (who have obtained their PhD degree in 2008 or after) can apply for a limited number of grants of 600 Euro for accommodation and traveling and will have their fees for the course waived.

  • In order to apply for the grants candidates should submit their CV by e-mail to
  • 1st deadline for applications: 15th July 2015. If there is any free place or some of the applicants fail to complete the attendance procedure, a second call will be open with deadline the 15th of September 2015.
  • Granted candidates will be informed by e-mail within a week after the deadline and must send their flight tickets and accommodation booking 15 days after the application deadlines (30th of July and 30th of September respectively) to to secure their grants. Otherwise, their grants will be revoked and assigned to other candidate.
  • The granted candidates must attend all the sessions of the course in order to obtain their grants.
Module I: Robust methods

Part I: An introduction to robust statistics.
Lecturers: Prof. Anthony Atkinson, London School of Economics, UK, and
Prof. Marco Riani, University of Parma, Italy.
Sessions 1, 2 and 3 of Module I.
Duration: 6 hours.
Abstract: Data corrupted by outliers are ubiquitous. Robust statistical methods are designed to provide fitted models which are unaffected by outliers. They also, ideally, should provide information about departures from the assumed model.
Each topic in the course will be introduced both algebraically and through examples. The topics will be illustrated in a variety of data settings using the highly interactive MATLAB toolbox FSDA, which is freely downloadable.
The robust methods to be described include downweighting methods, such as M and S estimation in which extreme observations are given reduced weight; hard trimming methods in which the best fit is obtained for a prespecified proportion of the data and flexible trimming using the forward search. We compare and contrast the properties of these methods in a variety of settings.
The course begins with multiple regression. We then consider robust transformations of the response. A second part is robust methods for multivariate data. We start with a single sample and then consider robust clustering.
The computer sessions will illustrate, in several of these settings, the use of linking and brushing plots to gain an understanding of how observations, individually or in clusters, are affecting conclusions drawn form the data.
An introductory text, now somewhat dated, is "Robust Diagnostic Regression Analysis" by A.C. Atkinson and M. Riani (Springer, 2000). The toolbox contains descriptions and examples of all techniques to be discussed.
Material: Regression Slides Multivariate Slides

Part II: Robust Methods for Econometrics
Title: Validity-Robust Semiparametrically Efficient Inference for Nonlinear Time Series Models
Lecturer: Prof. Marc Hallin, Universite Libre de Bruxelles, Belgium.
Sessions 4 and 5 of Module I.
Duration: 4 hours
Abstract: Nonlinear time series models play an important role in a number of econometric problems; they are pervasive in financial econometrics. Examples include AR-ARCH models, discretely observed non-Gaussian Ornstein-Uhlenbeck processes, autoregressive conditional duration models for irregularlysampled data, ... Although Gaussian assumptions, in that context, are quite unrealistic, Gaussian quasi-likelihood procedures (for estimation and testing) remain the most popular approach. Those methods, typically, are not validity-robust---namely, their validity (asymptotic probability level for tests, root-n consistency for estimators) is not guaranteed.
In principle, traditional semiparametric inference methods (in the style of Bickel et al. 1993) offer a theoretical alternative. However, they require tedious tangent space calculations, and the estimation of the actual innovation density. The objective of this tutorial is to show that rank-based inference constitutes a convenient substitute for those methods, and yield validity-robust tests and estimators reaching semiparametric efficiency bounds without running into the difficulties of tangent space calculation and density estimation.
Material: Paper1 Paper2 Paper3
Slides: Tutorial LeCam in a Nutshell Semiparametrics Regression Stable

Module II: Multivariate extreme value theory
Title: An offspring of multivariate extreme value theory: D-Norms
Lecturer: Prof. Michael Falk, University of Wuerzburg, Germany.
Sessions 1 to 5 of Module II.
Duration: 10 hours.
Abstract: Multivariate extreme value theory (MEVT) is the proper toolbox for analyzing several extremal events simultaneously. Its practical relevance in particular for risk assessment is, consequently, obvious. But on the other hand MEVT is by no means easy to access; its key results are formulated in a measure theoretic setup, a fils rouge is not visible.
Writing the 'angular measure' in MEVT in terms of a random vector, however, provides the missing fils rouge: Every result in MEVT, every relevant probability distribution, be it a max-stable one or a generalized Pareto distribution, every relevant copula, every tail dependence coefficient etc. can be formulated using a particular kind of norm on multivariate Euclidean space, called D-norm. Norms are introduced in each course on mathematics as soon as the multivariate Euclidean space is introduced. The definition of an arbitrary D-norm requires only the additional knowledge on random variables and their expectations. But D-norms do not only constitute the fils rouge through MEVT, they are of particular mathematical interest of their own.
In Sessions 1 to 3 we provide in the introductory chapter the theory of D-norms in detail. The second chapter introduces multivariate generalized Pareto distributions and max-stable distributions via D-norms. The third chapter provides the extension of D-norms to functional spaces and, thus, deals with generalized Pareto processes and max-stable processes.
Sessions 4 and 5, in addition to a brief summary of univariate EVT and D-norms, provides a relaxed tour through the essentials of MEVT, due to the D-norms approach.
Material: Booklet Slides
Organizers and chairs
CRoNos Action Core Management Group represented by
Erricos J. Kontoghiorghes, Stella Hadjiantoni and Ana Colubi.
Tentative programme

Wednesday, 9 December 2015

  • 13:45 – 14:00 Opening
  • 14:00 – 16:00 Session 1 of Module I
  • 16:00 – 16:30 Coffee break
  • 16:30 – 18:30 Session 1 of Module II

Thursday, 10 December 2015

  • 09:00 – 11:00 Session 2 of Module II
  • 11:00 – 11:30 Coffee break
  • 11:30 – 13:30 Session 3 of Module II
  • 13:30 – 15:00 Lunch break
  • 15:00 – 17:00 Session 2 of Module I
  • 17:00 – 17:30 Coffee break
  • 17:30 – 19:30 Session 3 of Module I

Friday, 11 December 2015

  • 09:00 – 11:00 Session 4 of Module II
  • 11:00 – 11:30 Coffee break
  • 11:30 – 13: 30 Session 5 of Module II
  • 13:00 – 15:00 Lunch break
  • 15:00 – 17:00 Session 4 of Module I
  • 17:00 – 17:30 Coffee break
  • 17:30 – 19:30 Session 5 of Module I