The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
P Values Maximized Over a Confidence Set for the Nuisance Parameter
Abstract
For testing problems of the form H 0: v = v 0 with unknown nuisance parameter θ, various methods are used to deal with θ. The simplest approach is exemplified by the t test where the unknown variance is replaced by the sample variance and the t distribution accounts for estimation of the variance. In other problems, such as the 2 × 2 contingency table, one conditions on a sufficient statistic for 0 and proceeds as in Fisher's exact test. Because neither of these standard methods is appropriate for all situations, this article suggests a new method for handling the unknown θ. This new method is a simple modification of the formal definition of a p value that involves taking a maximum over the nuisance parameter space of a p value obtained for the case when θ is known. The suggested modification is to restrict the maximization to a confidence set for the nuisance parameter. After giving a brief justification, we give various examples to show how this new method gives improved results for 2 × 2 tables and solves previously intractable semiparametric problems.
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Fluconazole prophylaxis against fungal colonization and infection in preterm infants.
Kaufman, D., Boyle, R., Hazen, K. C., Patrie, J. T., Robinson, M., & Donowitz, L. G. (2001) · New England Journal of Medicine · doi:10.1056/nejmoa010494 · Cited by 523
Estimating the tempo and mode of gene family evolution from comparative genomic data
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Greenland, S. (2019) · The American Statistician · doi:10.1080/00031305.2018.1529625 · Cited by 313
Metadata
- Authors
- Berger, R. L., & Boos, D. D.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1994-09
- Volume
- 89
- Issue
- 427
- Pages
- 1012-1016
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2055708265
- Cited by
- 317
- References
- 14
- FWCI
- 4.15
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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