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Hypothesis Testing for Arbitrary Bounds

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  • Jeffrey Penney

    ()
    (Queen's University)

Abstract

I derive a rigorous method to help determine whether a true parameter takes a value between two arbitrarily chosen points for a given level of confidence via a multiple testing procedure which strongly controls the familywise error rate. For any test size, the distance between the upper and lower bounds can be made smaller than that created by a confidence interval. The procedure is more powerful than other multiple testing methods that test the same hypothesis. This test can be used to provide an affirmative answer about the existence of a negligible effect.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1319.pdf
File Function: First version 2013
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Bibliographic Info

Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1319.

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Length: 6 pages
Date of creation: Oct 2013
Date of revision:
Handle: RePEc:qed:wpaper:1319

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Keywords: familywise error; multiple testing; null effect; partial identification; precise zero;

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  1. Richard M. Bittman & Joseph P. Romano & Carlos Vallarino & Michael Wolf, 2009. "Optimal testing of multiple hypotheses with common effect direction," Biometrika, Biometrika Trust, vol. 96(2), pages 399-410.
  2. Joseph P. Romano & Michael Wolf, 2005. "Stepwise Multiple Testing as Formalized Data Snooping," Econometrica, Econometric Society, vol. 73(4), pages 1237-1282, 07.
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