Smoothing in Ordinal Regression: An Application to Sensory Data
The so-called proportional odds assumption is popular in cumulative, ordinal regression. In practice, however, such an assumption is sometimes too restrictive. For instance, when modeling the perception of boar taint on an individual level, it turns out that, at least for some subjects, the effects...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
|
| Series: | Stats |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-905X/4/3/37 |
| _version_ | 1797517147310653440 |
|---|---|
| author | Ejike R. Ugba Daniel Mörlein Jan Gertheiss |
| author_facet | Ejike R. Ugba Daniel Mörlein Jan Gertheiss |
| author_sort | Ejike R. Ugba |
| collection | DOAJ |
| description | The so-called proportional odds assumption is popular in cumulative, ordinal regression. In practice, however, such an assumption is sometimes too restrictive. For instance, when modeling the perception of boar taint on an individual level, it turns out that, at least for some subjects, the effects of predictors (androstenone and skatole) vary between response categories. For more flexible modeling, we consider the use of a ‘smooth-effects-on-response penalty’ (SERP) as a connecting link between proportional and fully non-proportional odds models, assuming that parameters of the latter vary smoothly over response categories. The usefulness of SERP is further demonstrated through a simulation study. Besides flexible and accurate modeling, SERP also enables fitting of parameters in cases where the pure, unpenalized non-proportional odds model fails to converge. |
| first_indexed | 2024-03-10T07:12:41Z |
| format | Article |
| id | doaj.art-91cdd84654ba4aa5b057aab8a286a7cd |
| institution | Directory Open Access Journal |
| issn | 2571-905X |
| language | English |
| last_indexed | 2024-03-10T07:12:41Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Stats |
| spelling | doaj.art-91cdd84654ba4aa5b057aab8a286a7cd2023-11-22T15:18:20ZengMDPI AGStats2571-905X2021-07-014361663310.3390/stats4030037Smoothing in Ordinal Regression: An Application to Sensory DataEjike R. Ugba0Daniel Mörlein1Jan Gertheiss2Department of Mathematics and Statistics, School of Economics and Social Sciences, Helmut Schmidt University, 22043 Hamburg, GermanyDepartment of Animal Sciences, Faculty of Agricultural Sciences, Georg August University, 37077 Göttingen, GermanyDepartment of Mathematics and Statistics, School of Economics and Social Sciences, Helmut Schmidt University, 22043 Hamburg, GermanyThe so-called proportional odds assumption is popular in cumulative, ordinal regression. In practice, however, such an assumption is sometimes too restrictive. For instance, when modeling the perception of boar taint on an individual level, it turns out that, at least for some subjects, the effects of predictors (androstenone and skatole) vary between response categories. For more flexible modeling, we consider the use of a ‘smooth-effects-on-response penalty’ (SERP) as a connecting link between proportional and fully non-proportional odds models, assuming that parameters of the latter vary smoothly over response categories. The usefulness of SERP is further demonstrated through a simulation study. Besides flexible and accurate modeling, SERP also enables fitting of parameters in cases where the pure, unpenalized non-proportional odds model fails to converge.https://www.mdpi.com/2571-905X/4/3/37animal welfareBrant testcategorical dataquality controlregularizationsensometrics |
| spellingShingle | Ejike R. Ugba Daniel Mörlein Jan Gertheiss Smoothing in Ordinal Regression: An Application to Sensory Data Stats animal welfare Brant test categorical data quality control regularization sensometrics |
| title | Smoothing in Ordinal Regression: An Application to Sensory Data |
| title_full | Smoothing in Ordinal Regression: An Application to Sensory Data |
| title_fullStr | Smoothing in Ordinal Regression: An Application to Sensory Data |
| title_full_unstemmed | Smoothing in Ordinal Regression: An Application to Sensory Data |
| title_short | Smoothing in Ordinal Regression: An Application to Sensory Data |
| title_sort | smoothing in ordinal regression an application to sensory data |
| topic | animal welfare Brant test categorical data quality control regularization sensometrics |
| url | https://www.mdpi.com/2571-905X/4/3/37 |
| work_keys_str_mv | AT ejikerugba smoothinginordinalregressionanapplicationtosensorydata AT danielmorlein smoothinginordinalregressionanapplicationtosensorydata AT jangertheiss smoothinginordinalregressionanapplicationtosensorydata |