Abstract
Using the machinery of large and moderate deviations theory of empirical probability measures, we study effective importance sampling for simulation of large and moderate deviation probabilities of tests and estimators. The computational burden of efficient importance sampling does not have the exponential growth as in the straightforward simulation. The results are implemented in a simulation of moderate deviation probabilities of tests of omega-squared type.
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