Brief Biography: Dr. Good was born in Montreal, educated at McGill, and received his degrees in mathematical statistics at Berkeley. He has taught anatomy, biology, computer science, mathematics, and physics at the college level. He is the author of five statistics texts, 600+ popular articles on sports andcomputers, sixteen published short stories, and fifteen unpublished novels.

Recently, the class of experimental designs that are analyzable by permutation means (thus yielding exact p-values) was extended to the two-factor casethrough the use of synchronized permutations (Salmaso, 2003 and Pesarin 2001). The new tests achieve exact p-values through weak exchangeability(Good, 2002). By recognizing that synchronized permutations can be identified with similarities, these results can be extended to k-factor designs, balancedor not, complete or not.[full paper at: http://users.oco.net/drphilgood/synch02a.htm ]

We consider spatially interacting Moran models and their diffusion limit which are interacting Fisher-Wright diffusions. For both models the historical process is constructed, which gives information about genealogies. For any fixed time, particle representations for the historical process of a collection of Moran models with increasing particle intensity and of the limiting interacting Fisher-Wright diffusions are provided on one and the same probability space by means of a look-down process. It will be discussed how this can be used to obtain new results on the long term behavior. In particular, we give representations for the equilibrium historical processes. Based on the latter the behavior of large finite systems in comparison with the infinite system is described on the level of the historical processes. The talk is based on joint work with Andreas Greven and Vlada Limic.

Estimation of the number of stem cells involved in repopulationof the bone marrow following marrow transplantation usuallyrelies on methods of cell biology. The amount of stem cellsinvolved in this process may be a determinant of a numberof potential complications of the disease.Statistical methods relying upon some simple assumptionsconcerning cell dynamics can also be used. The problem thenbecomes one which can be framed in a context of binomialsampling. However, unlike the more classic problem where wecondition upon some value n and make inference on a populationparameter p based on the observed number of successes, hereinference is focussed upon n. We are unable to observe thenumber of successes directly. Bayesian methods are also possible.