Generalized linear models for ordered categorical data
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| Title: | Generalized linear models for ordered categorical data |
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| Authors: | Holm, Sture, 1936 |
| Source: | Communications in Statistics - Theory and Methods. 52(3):670-683 |
| Subject Terms: | Generalized linear model, scale data, rank methods |
| Description: | Categorical scale data are only ordinal and defined on a finite set. Continuous scale data are only ordinal and defined on a bounded interval. Due to that character, the statistical methods for scale data ought to be based on orders between outcomes only and not any metric involving distance measure. For simple two-sample scale data, variants of classical rank methods are suitable. For regression type of problems, there are known good generalized linear models for separate categories for a long time. In the present article is suggested a new generalized linear type of model based on non parametric statistics for the whole scale. Asymptotic normality for those statistics is also shown and illustrated. Both fixed and random effects are considered. |
| File Description: | electronic |
| Access URL: | https://research.chalmers.se/publication/524119 https://research.chalmers.se/publication/524119/file/524119_Fulltext.pdf |
| Database: | SwePub |
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Nájsť tento článok vo Web of Science | Abstract: | Categorical scale data are only ordinal and defined on a finite set. Continuous scale data are only ordinal and defined on a bounded interval. Due to that character, the statistical methods for scale data ought to be based on orders between outcomes only and not any metric involving distance measure. For simple two-sample scale data, variants of classical rank methods are suitable. For regression type of problems, there are known good generalized linear models for separate categories for a long time. In the present article is suggested a new generalized linear type of model based on non parametric statistics for the whole scale. Asymptotic normality for those statistics is also shown and illustrated. Both fixed and random effects are considered. |
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| ISSN: | 03610926 1532415X |
| DOI: | 10.1080/03610926.2021.1921210 |