"Solving Unusual Problems in Statistics"

	In practice, models of data are often inadequate for
	problems in regression, clustering, data mining, etc.
	A few algorithms hint at the challenges (and the
	opportunities).  For example, algorithms exist for
	fitting "mixtures of regressions."  The mclust
	algorithm in Splus allows for Poisson noise while
	fitting a mixture of normal densities.

	In each case, an unknown subset of the data is "bad."
	How should we know the model of the bad data?  What is
	an appropriate model for the regression curve of the
	bad data?  What if the bad data are not Poisson in
	the mixture problem?  How can one identify multiple
	outliers in a data mining setting?

	I describe some algorithms that only require a
	model of the good data and are not affected by the
	particular shape of the bad data in many situations.
	Applications of these ideas are surprisingly diverse.
	For example, in a high-dimensional clustering problem,
	can I find the largest eigenvector of one of the
	clusters without fitting a complete mixture model?
	Come to this talk and see what is possible today.