Asymptotic optimality of data-driven Neyman's tests for uniformity
作者： Teresa Ledwina , Tadeusz Inglot
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摘要: Data-driven Neyman's tests resulting from a combination of smooth for uniformity and Schwarz's selection procedure are investigated. Asymptotic intermediate efficiency those with respect to the Neyman-Pearson test is shown be 1 large set converging alternatives. The result shows that data-driven tests, contrary classical goodness-of-fit indeed omnibus adapting well data at hand.
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