Methoden vergleichen
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| Ordinal-Logistische Regression× | Quantile Regression× | |
|---|---|---|
| Fachgebiet≠ | Statistik | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1980 | 1978 |
| Urheber≠ | Peter McCullagh | Koenker & Bassett |
| Typ≠ | Ordinal regression / GLM | Conditional quantile regression |
| Wegweisende Quelle≠ | McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Aliasnamen≠ | proportional-odds model, cumulative link model, ordered logit, OLR | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | 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. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
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