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Empirical process theory tools for statistics

Date: 10:00 29 October 2015 - 17:00 30 October 2015
Type: Masterclass
Tutor: Jon Wellner University of Washington
Venue: Institute for Fiscal Studies
Prices: HE delegates: £75; Charity/Government: £200; other delegates: £450; All prices are exclusive of VAT


This course will cover some of the basics of empirical process theory and the application of the theory to problems in statistics. The focus will be on some of the basic convergence theory and methods together with inequalities for dealing with minimum contrast and maximum likelihood estimators in nonparametric and semiparametric models.


Day 1

  • Introduction, history, selected examples
  • Some basic inequalities
  • Glivenko-Cantelli theorems and rst applications


Day 2

  • Donsker theorems and some inequalities
  • Peeling methods and rates of convergence
  • Some useful preservations theorems


Some References

1. Van der Vaart, A. W. and Wellner, J. A. (1996). Weak Convergence and Empirical Processes. Springer, New York.

2. Dudley, R. M. (1999). Uniform Central Limit Theorems. Cambridge University Press, Cambridge.

3. Van de Geer, S. (2000). Applications of Empirical Process Theory. Cambridge University Press, Cambridge.

4. de la Pena, V. H., and Gin e, E. (1999). Decoupling: From Dependence to Independence. Springer, New York.

5. Wellner, J. A. (2003 - 2005). Empirical Processes: Theory and Applications. 

Notes available on-line at: http://www.stat.washington.edu/jaw/RESEARCH/TALKS/talks.html 


This is event is jointly organised by the Cambridge-INET Institute.

General information

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