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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20211022T123809Z
DTSTART:20211124T140000Z
DTEND:20211124T150000Z
SUMMARY:Christophe Ley  - Directional statistics and protein bioinformati
 cs: a flexible approach
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}jkw-ku8cdrj
 d-5a948j
DESCRIPTION:Christophe Ley\, Associate Professor of Statistics at the Dep
 artment of Applied Mathematics\, Computer Science and Statistics at Ghen
 t University is our speaker for the Statistics seminar series.\n\nTitle:
  Directional statistics and protein bioinformatics: a flexible approach\
 n\nAbstract: In the bioinformatics field\, there has been a growing inte
 rest in modelling dihedral angles of amino acids by viewing them as data
  on the torus. This has motivated\, over the past years\, new proposals 
 of distributions on the bivariate torus. The main drawback of most of th
 ese models is that the related densities are (pointwise) symmetric\, des
 pite the fact that the data usually present asymmetric patterns. This mo
 tivates the need to find a new way of constructing asymmetric toroidal d
 istributions starting from a symmetric distribution. We tackle this prob
 lem in this paper by introducing the sine-skewed toroidal distributions.
  The general properties of the new models are presented. An important fe
 ature of our construction is that no normalizing constant needs to be ca
 lculated\, leading to more flexible distributions without increasing the
  complexity of the models. The benefit of employing these new sine-skewe
 d distributions is shown on the basis of protein data\, where\, in gener
 al\, the new models outperform their symmetric antecedents. A word about
  Bayesian inference shall also be said\, if the time permits it.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Zoom link: https://zoom.us/j/92947173491
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