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Regression, Discrimination and Measurement Models for Ordered Categorical Variables
Abstract
SUMMARY Regression models for the analysis of ordered categorical variables are discussed with particular reference to the logistic model. Maximum likelihood estimation procedures are established for three common sampling plans including the case where sampling is conditional on the ordered variable. This was previously not available. The use of these models in discrimination is discussed and an example given. An original method for the establishment of rating scales based on a coarse direct assessment and related variables is introduced. Mention is made of some difficulties in the application of the models and of their potential use for the analysis of designed experiments with ordered response variables.
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Cited by
Decision combination in multiple classifier systems
Ho, T. K., Hull, J. J., & Srihari, S. N. (1994) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.273716 · Cited by 1,511
On the existence of maximum likelihood estimates in logistic regression models
ALBERT, A., & ANDERSON, J. A. (1984) · Biometrika · doi:10.1093/biomet/71.1.1 · Cited by 1,153
Self-citationGreen, P. J. (1984) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1984.tb01288.x · Cited by 687
Regression models for ordinal responses: a review of methods and applications
Ananth, C. (1997) · International Journal of Epidemiology · doi:10.1093/ije/26.6.1323 · Cited by 593
Regression and ordered categorical variables
Anderson, J. A. (1984) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1984.tb01270.x · Cited by 574
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Metadata
- Authors
- Anderson, J. A., & Philips, P. R.
- Venue
- Applied Statistics
- Publisher
- JSTOR
- Published
- 1981
- Volume
- 30
- Issue
- 1
- Pages
- 22
- ISSN
- 0035-9254 (ISSN-L)
- OpenAlex
- W2441062895
- Cited by
- 194
- References
- 0
- FWCI
- 5.24
- Type
- journal article
- Language
- en
- Open access
- closed
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