The penalty in data driven Neyman's tests

W.C.M. Kallenberg

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Abstract

Data driven Neyman's tests are based on two elements: Neyman's smooth tests in finite dimensional submodels and a selection rule to choose the ``right'' submodel. As selection rule usually (a modification of) Schwarz's rule is applied. In this paper we consider data driven Neyman's tests with selection rules allowing also other penalties than the one in Schwarz's rule. It is shown that the nice properties of consistency against very large classes of alternatives and the more deep result of asymptotic optimality in the sense of vanishing shortcoming continue to hold for other penalties as well, including the one corresponding to Akaike's selection rule.
Original languageUndefined
Place of PublicationEnschede
PublisherUniversity of Twente, Department of Applied Mathematics
Publication statusPublished - 2000

Publication series

Name
PublisherDepartment of Applied Mathematics, University of Twente
No.1548
ISSN (Print)0169-2690

Keywords

  • EWI-3368
  • IR-65735
  • MSC-62G10
  • MSC-62G20

Cite this

Kallenberg, W. C. M. (2000). The penalty in data driven Neyman's tests. Enschede: University of Twente, Department of Applied Mathematics.
Kallenberg, W.C.M. / The penalty in data driven Neyman's tests. Enschede : University of Twente, Department of Applied Mathematics, 2000.
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Kallenberg, WCM 2000, The penalty in data driven Neyman's tests. University of Twente, Department of Applied Mathematics, Enschede.

The penalty in data driven Neyman's tests. / Kallenberg, W.C.M.

Enschede : University of Twente, Department of Applied Mathematics, 2000.

Research output: Book/ReportReportOther research output

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Kallenberg WCM. The penalty in data driven Neyman's tests. Enschede: University of Twente, Department of Applied Mathematics, 2000.