Dear All,
there has been a change of speaker for the seminar scheduled for the 9th of March - the schedule is now (room 139, Huxley Building at Imperial College London):
9th March
14.00 Dr Michael Stumpf, Theoretical Genomics, Imperial College London
Sampling properties of random graphs and biological networks
Most biological network datasets - whether protein interaction or transcriptional networks - only describe parts of the available interactions. Here we show that the properties of subnets drawn from networks are generally very different from the properties of the true network. We can derive basic necessary and sufficient conditions under which the subnet will be of the same type as the true network. Unfortunately these conditions are not fulfilled for realistic network ensembles. Nevertheless under some circumstances it is possible to use multi-model inference techniques in order to infer properties of the real network from partial network data.
15.30 Professor Jerry Lawless, Department of Statistics and Actuarial Science, University of Waterloo
Problems in Survival and Event History Analysis Arising From Intermittent Followup of Individuals
Glenn Shafer's talk has been rescheduled for later this year.
Please note also the following scheduled seminars:
11th May
14.00 Michael Wolf, Econometrics and Applied Statistics, Universität Zürich
Formalized Data Snooping Based on Generalized Error Rates
It is common in econometric applications that several hypothesis tests are carried out simultaneously. The problem then becomes how to decide which hypotheses to reject, accounting for the multitude of tests. The classical approach is to control the familywise error rate (FWE) which is the probability of one or more false rejections. But when the number of hypotheses under consideration is large, control of the FWE can become too demanding. As a result, the number of false hypotheses rejected may be small or even zero. This suggests replacing control of the FWE by a more liberal measure. To this end, we review a number of recent proposals from the statistical literature. We briefly discuss how these procedures apply to the general problem of model selection. A simulation study and two empirical applications illustrate the methods.
15.30 Karim Abadir, Tanaka Business School, Imperial College London
Semiparametric estimation and inference for trending I(d) and related processes.
We deal with estimation and hypothesis testing in models allowing for trending processes that are possibly nonstationary, nonlinear, and non-Gaussian. Using semiparametric estimators, we obtain asymptotic confidence intervals for the trend and memory parameters, and we develop joint hypothesis testing for these. The confidence intervals are applicable for a wide class of processes, exhibit high coverage accuracy, and are easy to implement.
This is joint work with W. Distaso, and L. Giraitis.
Kind regards
Sofia
Dr Sofia Olhede
Senior Lecturer in Statistics,
Department of Mathematics, Imperial College London 525, Huxley Building
South Kensington Campus London, SW7 2AZ, UK
[log in to unmask]
http://www3.imperial.ac.uk/people/s.olhede
ICIAM 2007: http://stats.ma.ic.ac.uk/sco/public_html/iciam07scale.html
RSS 2008: http://stats.ma.ic.ac.uk/sco/public_html/rssordinary.html
LTCC: http://stats.ma.ic.ac.uk/sco/public_html/LTCC.htm
RSS 2007: http://stats.ma.ic.ac.uk/sco/public_html/rsscourse.htm
Seminars: http://stats.ma.ic.ac.uk/sco/public_html/seminarsIC.htm
|