[Statlist] talks on statistics

[Christina Kuenzli]

                  ETH and University of Zurich 

                           Proff. 
         A.D. Barbour - P. Buehlmann - F. Hampel - L. Held

           H.R. Kuensch - M. Maathuis - S. van de Geer

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           We are glad to announce the following talks
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         Friday, May 9, 2008, 15.15-17.00, LEO C6 

         Estimation of Optimal Dynamic Anticoagulation Regimes from
         Observational Data: A Regret-Based Approach
         Robin Henderson, Newcastle University, UK

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         Friday, May 16, 2008, 15.15-17.30, LEO C6
 
         Non-asymptotic variable identification via the Lasso 
         and the elastic net
         Florentina Bunea, Florida State University  

  The topic of $\ell_1$ regularized or Lasso type estimation has received
  considerable attention over the past decade. Recent theoretical advances
  have been mainly concerned with the risk of the estimators and
  corresponding sparsity oracle inequalities. In this talk we will
  investigate the quality of the $\ell_1$ penalized estimators from a
  different perspective, shifting the emphasis to non-asymptotic variable
  selection, which complements the consistent variable selection
  literature. Our main results are established for regression models, with
  emphasis on the square and logistic loss. The identification of the
  tagged SNPs associated with a disease, in genome wide association
  studies, provides the principal motivation for this analysis. The
  performance of the method depends crucially on the choice of the tuning
  sequence and we discuss non-asymptotic choices for which we can correctly
  detect sets of variables associated with the response at any
  pre-specified confidence level. These tuning sequences are different for
  the two loss functions, but in both cases larger than those required for
  best risk performance. The stability of the design matrix is another
  major issue in correct variable selection, especially when the total
  number of variables exceeds the sample size. A possible solution is
  provided by further regularization, for instance via an $\ell_1 + \ell_2$
  or elastic net type penalty. We discuss the merits and limitations of
  this method in the same context as above.
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          Friday, May 23, 2008, 15.15-16.00, LEO C6

          Conditioned Limit Theorems:
          Does the Story End with a Bang or a Whimper?
          Sidney Resnick, Cornell University, Ithaca 

Multivariate extreme value theory assumes a multivariate domain of
attraction condition for the distribution of a random vector necessitating
that each component satisfy a marginal domain of attraction condition.
Heffernan and Tawn (2004) followed by Heffernan and Resnick (2007)
developed an approximation to the joint distribution of the random vector by
conditioning that  one of  the components be extreme. Prior papers left
unresolved the consistency of different models obtained by conditioning on
different components being extreme and we provide understanding of this
issue. We also clarify the relationship between the conditional
distributions and  multivariate extreme value theory. We discuss conditions
under which the two models are the same and when one can extend the
conditional model to the extreme value model. We also discuss the
relationship between the conditional extreme value model and standard
regular variation on different cones.
Joint work with B. Das (Cornell) and J. Heffernan (Lancaster)

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         Friday, May 23, 2008, 16.15-17.00, LEO C6

         Design and analysis of time to pregnancy
         Niels Keiding, University of Copenhagen, DK

  Time to pregnancy is the duration from a couple starts trying to become
  pregnant until they succeed and is considered one of the most direct
  methods to measure natural fecundity in humans. Statistical tools for
  designing and analysing time to pregnancy studies belong to the general
  area of survival analysis, but several special features require special
  attention. I will survey prospective designs, including historically
  prospective and prevalent cohort, retrospective (pregnancy-based)
  designs, and focus particularly on the possibilities to start from a
  cross-sectional sample of couples currently trying to be come
  pregnant. The latter case corresponds to using the backward recurrence
  time as basis for the inference, and here the preferable statistical
  model turns out to be the accelerated failure time model.
  
  The talk will be illustrated by examples from our own experience.

  References:

  Keiding, N., Kvist, K., Hartvig, H., Tvede, M. & Juul,
S. (2002). Estimating time to pregnancy from current durations in a
cross-sectional sample. Biostatistics 3, 565-578. 

  Scheike, T. & Keiding, N. (2006). Design and analysis of time to
pregnancy. \textit{Stat. Meth. Med. Res.  15, 127-140. 
  
_______________________________________________________
Christina Kuenzli            <kuenzli at stat.math.ethz.ch>
Seminar fuer Statistik      
Leonhardstr. 27,  LEO D11      phone: +41 (0)44 632 3438         
ETH-Zentrum,                   fax  : +41 (0)44 632 1228 
CH-8092 Zurich, Switzerland        http://stat.ethz.ch/~