Van Dantzig Seminar
|When:||13th of October 2017 tot 16th of September 2017|
VAN DANTZIG SEMINAR
On Friday October 13 the Van Dantzig Seminar on statistics will host lectures by Andrew Parnell (University College Dublin) and Alberto Roverato (University of Bologna).
The program is (titles and abstracts below):
14.00 - 14.05 opening
14.05 - 15.05 Andrew Parnell
15.05 - 15.25 break
15.25 - 16.25 Alberto Roverato
16.30 - 17.30 drinks
Location: Amsterdam Science Park Congress Centre, room: Eulerzaal. Address: Science Park 123, Amsterdam.
Remark: Entrance to Amsterdam Science Park Congress Centre is right next to the main entrance of CWI. See: http://www.wcw.nl/Congreszalen/
The Van Dantzig seminar is a nationwide series of lectures in statistics, that features renowned international and local speakers from the full breadth of the statistical sciences. The name honours David van Dantzig (1900-1959), who was the first modern statistician in the Netherlands, and professor in the "Theory of Collective Phenomena" (i.e. statistics) in Amsterdam. The seminar will convene 4 to 6 times a year at varying locations, and is financially supported by, among others, the STAR cluster and the Section Mathematical Statistics of the VVS-OR.
Everybody is cordially invited to attend.
TITLES AND ABSTRACTS OF PRESENTATIONS
University College Dublin
How fast is sea level rising?
Measuring sea level rise is vitally important in future infrastructure planning and for measuring the impact of climate change. The current estimate by the Intergovernmental Panel for Climate Change is that global sea level is rising at approximately 1.7mm per year. However, this rise seems to be accelerating, and is widely variable in different locations. We use tide gauge data and biological sea level markers to answer questions such as: how fast is sea level acceleration? Is this rise unprecedented in the recent past? The models we build make use of Bayesian methods to combine the disparate data sets and produce sea level estimates with quantified levels of uncertainty.
University of Bologna
Path weights, networked partial correlations and their application to the analysis of genetic interactions
Genetic interactions confer robustness on cells in response to genetic perturbations. This often occurs through molecular buffering mechanisms that can be predicted using, among other features, the degree of coexpression between genes, commonly estimated through marginal measures of association such as Pearson or Spearman correlation coefficients. However, marginal correlations are sensitive to indirect effects and often partial correlations are used instead. Yet, partial correlations convey no information about the (linear) influence of the coexpressed genes on the entire multivariate system, which may be crucial to discriminate functional associations from genetic interactions. To address these two shortcomings, here we propose to use the edge weight derived from the covariance decomposition over the paths of the associated gene network. We call this new quantity the networked partial correlation and use it to analyse genetic interactions in yeast. More concretely, in its well-characterized leucine biosynthesis pathway and on a previously published data set of genome-wide quantitative genetic interaction profiles. In both cases, networked partial correlations substantially improve the identification of genetic interactions over classical coexpression measures.
This talk is based on a joint work with Robert Castelo, University Pompeu Fabra, Spain.