Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Ordinālā loģistiskā regresija× | Vispārīgais lineārais modelis (GLM)× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1980 | 1972 |
| Autors≠ | Peter McCullagh | John A. Nelder & Robert W. M. Wedderburn |
| Tips≠ | Ordinal regression / GLM | Regression framework |
| Pirmavots≠ | McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142. DOI ↗ | Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384. DOI ↗ |
| Citi nosaukumi | proportional-odds model, cumulative link model, ordered logit, OLR | GLM, generalized regression, exponential family regression, link-function model |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds. | The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case. |
| ScholarGateDatu kopa ↗ |
|
|