On optimal tests for general interval-hypotheses
- 1 January 1993
- journal article
- research article
- Published by Taylor & Francis in Communications in Statistics - Theory and Methods
- Vol. 22 (5) , 1257-1297
- https://doi.org/10.1080/03610929308831086
Abstract
Optimal statistical tests, using the normality assumptions for general interval hypotheses including equivalence testing and testing for nonzero difference (or for non-unit) are presented. These tests are based on the decision theory for Polya Type distributions and are compared with usual confidence tests and with ’two one-sided tests’- procedures. A formal relationship between some optimal tests and the Anderson and Hauck procedure as well as a procedure recommended by Patel and Gupta is given. A new procedure for a generalisation of Student's test as well as for equivalence testing for thet-statistics is shown.Keywords
This publication has 13 references indexed in Scilit:
- On a Special Feature of the Patel‐Gupta Equivalence TestBiometrical Journal, 1991
- General Approaches to the Problem of BioequivalenceJournal of the Royal Statistical Society: Series D (The Statistician), 1988
- A comparison of the Two One-Sided Tests Procedure and the Power Approach for assessing the equivalence of average bioavailabilityJournal of Pharmacokinetics and Biopharmaceutics, 1987
- On level and powee of anderson and hauck's peocedure for testing equivalence in compative bioavailabilityCommunications in Statistics - Theory and Methods, 1987
- Testing Statistical HypothesesPublished by Springer Nature ,1986
- A Problem of Equivalence in Clinical TrialsBiometrical Journal, 1984
- A new procedure for testing equivalence in comparative bioavailability and other clinical trialsCommunications in Statistics - Theory and Methods, 1983
- Polya Type Distributions, IIThe Annals of Mathematical Statistics, 1957
- DECISION THEORY FOR PÓLYA TYPE DISTRIBUTIONS. CASE OF TWO ACTIONS, IPublished by University of California Press ,1956
- Some Difficulties of Interpretation Encountered in the Application of the Chi-Square TestJournal of the American Statistical Association, 1938