COMPUTATIONAL METHODS FOR EVALUATING SEQUENTIAL TESTS AND POST-TEST ESTIMATION VIA THE SUFFICIENCY PRINCIPLE

By the sufficiency principle, the probability density of a sequential test statistic under certain conditions can be factored into a known function that does not depend on the stopping rule and a conditional probability that is free of unknown parameters. We develop general theorems and propose a un...

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Bibliographic Details
Published in:Statistica Sinica 2002-10, Vol.12 (4), p.1027-1041
Main Authors: Xiong, Xiaoping, Tan, Ming, Kutner, Michael H.
Format: Article
Language:English
Subjects:
Clinical trials
Conditional probabilities
Eigenvalues
Estimation methods
Maximum likelihood estimation
Probabilities
Sample size
Sequential analysis
Statistical theories
Statistics
Online Access:Get full text
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Summary:By the sufficiency principle, the probability density of a sequential test statistic under certain conditions can be factored into a known function that does not depend on the stopping rule and a conditional probability that is free of unknown parameters. We develop general theorems and propose a unified approach to analyzing and evaluating various properties of sequential tests and post-test estimation. The proposed approach is of practical value since it allows for effective evaluation of properties of special interest, such as the bias-adjustment of post-test estimation after a sequential test, and the probability of discordance between a sequential test and a nonsequential test.
ISSN:1017-0405
1996-8507