The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The Regression Analysis of Binary Sequences
Abstract
Summary: A sequence of 0’s and 1’s is observed and it is suspected that the chance that a particular trial is a 1 depends on the value of one or more independent variables. Tests and estimates for such situations are considered, dealing first with problems in which the independent variable is preassigned and then with independent variables that are functions of the sequence. There is a considerable amount of earlier work, which is reviewed.
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Cites
Some Methods for Strengthening the Common χ 2 Tests
Cochran, W. G. (1954) · Biometrics · doi:10.2307/3001616 · Cited by 3,718
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Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models
Jaeger, T. F. (2008) · Journal of Memory and Language · doi:10.1016/j.jml.2007.11.007 · Cited by 3,317
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BackgroundMethodThe accuracy, fairness, and limits of predicting recidivism
Dressel, J., & Farid, H. (2018) · Science Advances · doi:10.1126/sciadv.aao5580 · Cited by 1,071
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036
Two Further Applications of a Model for Binary Regression
Cox, D. R. (1958) · Biometrika · doi:10.2307/2333203 · Cited by 557
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Metadata
- Authors
- Cox, D. R.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1958-07-01
- Volume
- 20
- Issue
- 2
- Pages
- 215-232
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2903950532
- Cited by
- 2,813
- References
- 1
- FWCI
- 4.40
- 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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
