The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The Detection of Partial Association, I: The 2 × 2 Case
Abstract
Summary: A criterion is derived for testing null hypotheses of conditional independence of two dichotomous random variables. This test has optimum properties when the alternative hypothesis is that the association of the two random variables in the conditional distribution is, in a certain sense, constant.
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Citations
Cites
Some Methods for Strengthening the Common χ 2 Tests
Cochran, W. G. (1954) · Biometrics · doi:10.2307/3001616 · Cited by 3,718
Cited by
The Multivariate Analysis of Qualitative Data: Interactions among Multiple Classifications
Goodman, L. A. (1970) · Journal of the American Statistical Association · doi:10.1080/01621459.1970.10481076 · Cited by 746
Han, B., & Eskin, E. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.04.014 · Cited by 680
Lee, C. H., Cook, S., Lee, J. S., & Han, B. (2016) · Genomics & Informatics · doi:10.5808/gi.2016.14.4.173 · Cited by 308
Efficiency of Case-Control Studies with Multiple Controls Per Case: Continuous or Dichotomous Data
Ury, H. K. (1975) · Biometrics · doi:10.2307/2529548 · Cited by 249
Lachin, J. M., & Bautista, O. M. (1995) · Cancer Treatment and Research · doi:10.1007/978-1-4615-2009-2_10 · Cited by 12
Metadata
- Authors
- Birch, M. W.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1964-07-01
- Volume
- 26
- Issue
- 2
- Pages
- 313-324
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2512186195
- Cited by
- 198
- References
- 1
- FWCI
- 3.32
- Type
- journal article
- Language
- en
- Open access
- closed
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- Birch, M. W. (1964). The Detection of Partial Association, I: The 2 × 2 Case. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/j.2517-6161.1964.tb00564.x
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
