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Abstract:
In this talk we first briefly review some basic ideas on statistical model checking based on principal components of stochastic processes. We then show how to proceed when, in addition, the data allow for measurement errors. It becomes clear that in a testing situation we are led to a well-posed rather than, as in estimation, an ill-posed problem.

This is joint work with H. Kaiser (Giessen).