Vanishing shortcoming of data driven Neyman's tests

Tadeusz Inglot, B. Szyszkowicz (Editor), W.C.M. Kallenberg, Teresa Ledwina

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    The shortcoming of a test is the difference between the power of the test and the power of the most powerful test. For a large set of alternatives converging to the null hypothesis asymptotic optimality of data driven Neyman's tests is shown in terms of vanishing shortcoming when the level of signicance tends to zero. In contrast to classical goodness-of-fit tests data driven Neyman's tests are asymptotically efficient in an infinite number of orthogonal directions.
    Original languageUndefined
    Number of pages19
    Publication statusPublished - 1998
    EventAsymptotic Methods in Probability and Statistics - Ottawa
    Duration: 8 Jul 199713 Jul 1997


    ConferenceAsymptotic Methods in Probability and Statistics
    Other8-13 July 1997


    • intermediate eciency
    • MSC-62G10
    • IR-62414
    • Shortcoming
    • Schwarz's criterion
    • Large deviations
    • MSC-62G20
    • smooth test
    • EWI-13167

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