Please note that the seminar has been cancelled. We apologise for the inconvenience caused.
The notion of stationarity has more a mathematical origin that a tight relationship to real data sets. Namely the underlying idea of this assumption is the use of the ergodic theorem (the law of large numbers). The aim of the talk is to try to provide mathematical models adapted to several issues of real data. We aim also at precisely setting some technical ideas for fitting such models. We will describe some models for astronomical data sets, for actuarial models for lifetables, in order to exhibit precise features of interest for real models, and we will try to avoid the standard mathematical traps to pass from stationary models to non-stationary ones. Namely local stationarity, periods, exogenous data and isotonic assumptions are clearly seen to be reasonable. Weak dependence conditions are also quite valuable in such settings.
Professor Paul Doukhan is a prominent researcher in the field of dependence modeling, and a professor at University Cergy-Pontoise with the highest rank. His Google citation index is over 6400, and his 1994 book on Mixing Properties and Examples is cited more than 2000 times. In 1999, he introduced weak dependence as a fruitful alternative to Strong Mixing conditions and provided various applications of his technique to many areas of science. His skills range from Mathematical analysis (with introduction of wavelets in statistic) to biological modeling, astronomy, as well as to finance and actuarial sciences. He was awarded as an IUF (Institut Universitaire de France) Senior Member in 2011, which corresponds to a selection of less than 3% of French academics (only 3 professors of statistics have received this award).
For more information about the ESD Seminars Series, please contact Lin Shao Wei at firstname.lastname@example.org.