Hypothesis Testing for Arbitrary Bounds
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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- Joseph P. Romano & Michael Wolf, 2005.
"Stepwise Multiple Testing as Formalized Data Snooping,"
Econometric Society, vol. 73(4), pages 1237-1282, July.
- Joseph P. Romano & Michael Wolf, 2003. "Stepwise multiple testing as formalized data snooping," Economics Working Papers 712, Department of Economics and Business, Universitat Pompeu Fabra.
- Joseph P. Romano & Michael Wolf, 2003. "Stepwise Multiple Testing as Formalized Data Snooping," Working Papers 17, Barcelona Graduate School of Economics.
- Richard M. Bittman & Joseph P. Romano & Carlos Vallarino & Michael Wolf, 2009.
"Optimal testing of multiple hypotheses with common effect direction,"
Biometrika Trust, vol. 96(2), pages 399-410.
- Richard M. Bittman & Joseph P. Romano & Carlos Vallarino & Michael Wolf, 2008. "Optimal testing of multiple hypotheses with common effect direction," IEW - Working Papers 307, Institute for Empirical Research in Economics - University of Zurich.
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