Partial Proportional Odds Models for Ordinal Response Variables
Clicks: 3
ID: 305143
1990
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
0.6
/100
3 views
2 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #44 of 46 articles by views in Journal of the Royal Statistical Society Series C (Applied Statistics)
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
SUMMARY The ordinal logistic regression model that McCullagh calls the proportional odds model is extended to models that allow non-proportional odds for a subset of the explanatory variables. The maximum likelihood method is used for estimation of parameters of general and restricted partial proportional odds models as well as for the derivation of Wald, Rao score and likelihood ratio tests. These tests assess association without assuming proportional odds and test proportional odds against various alternatives. Simulation results compare the score test for proportional odds with tests suggested by Koch, Amara and Singer that are based on a series of binary logistic models.
| Reference Key |
openalex_W192858080
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Bercedis L. Peterson, Frank E. Harrell |
| Journal | Journal of the Royal Statistical Society Series C (Applied Statistics) |
| Year | 1990 |
| DOI |
10.2307/2347760
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.