Vanishing shortcoming of data driven Neyman's tests

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

    Research output: Contribution to conferencePaperAcademicpeer-review

    Abstract

    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
    Pages811-829
    Number of pages19
    Publication statusPublished - 1998
    EventAsymptotic Methods in Probability and Statistics - Ottawa
    Duration: 8 Jul 199713 Jul 1997

    Conference

    ConferenceAsymptotic Methods in Probability and Statistics
    Period8/07/9713/07/97
    Other8-13 July 1997

    Keywords

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

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