European Courses in Advanced Statistics - Courses

The Tenth Course in the ECAS Programme:

Scientific Programme Committee:

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J. Beirlant, Katholieke Universiteit Leuven, Belgium
J.-J. Droesbeke, Université Libre de Bruxelles, Belgium
M. Hallin, Université Libre de Bruxelles, Belgium
S.Heiler, Universitat Konstanz, Germany
H. Oja, University of Tampere, Finlande
L. Simar, Université Catholique de Louvain, Belgium



Organising Committee:

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D. Cassart, Université Libre de Bruxelles, Belgium
C. Dehon, Université Libre de Bruxelles, Belgium
J.-J. Droesbeke, Université Libre de Bruxelles, Belgium
M. Hallin, Université Libre de Bruxelles, Belgium
D. Paindaveine, Université Libre de Bruxelles, Belgium
C. Vermandele, Université Libre de Bruxelles, Belgium



Scope of the course:

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Quantile regression is a statistical technique intended to estimate, and to conduct inference about, conditional quantile functions. Just as classical linear regression methods based on minimizing sums of squared residuals enables one to estimate models for conditional mean functions, regression quantile methods offer a mechanism for estimating models for the conditional median function, and the full range of conditional quantile functions. Estimation here is based on a weighted sum (with weights depending on the order of the quantiles) of absolute values of residuals. By supplementing, the conditional mean with an entire collection of conditional quantiles, regression quantile methods provide a much more complete statistical analysis of the stochastic relationships among variables; in addition, they are more robust against possible outliers, and can be computed via traditional linear programming methods. Although median regression ideas go back to the 18th century and the work of Laplace, regression quantile methods were first introduced by Koenker and Bassett (1978) in a seminal Econometrica paper. Since then, they have generated a huge literature, and have found innumerable applications. Along with the dual methods of regression rank scores, regression quantiles can be considered one of the major statistical breakthroughs of the past thirty years.


Topics:

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• Introduction.
  R. Koenker (University of Illinois, USA)
  
  
• Fundamentals.
  R. Koenker (University of Illinois, USA)
  
  
• Asymptotics.
  I. Mizera (University of Alberta, CANADA)
  
  
• Rank Based Inference.
  B. Werker (University of Tilburg, NETHERLAND)
  
  
• Introduction of QR Inference.
  R. Koenker (University of Illinois, USA)
  
  
• Nonparametrics.
  S. Heiler (University of Konstanz, GERMANY)
  
  
• Penalty Methods for Nonparametrics.
  I. Mizera (University of Alberta, CANADA)
  
  
• Longitudinal Data Analysis.
  R. Koenker (University of Illinois, USA)
  
  
• Multivariate Analysis and Data Depth.
  I. Mizera (University of Alberta, CANADA)
  
  
• Time-Series Analysis.
  B. Werker (University of Tilburg, NETHERLAND)
  
  
• Survival Analysis and Binary Response.
  R. Koenker (University of Illinois, USA)
  
  
• Tutorial on Computational Methods.
  R. Koenker (University of Illinois, USA) and I. Mizera (University of Alberta, CANADA)
  
  

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