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| Ординална логистична регресия× | Квантилна регресия× | |
|---|---|---|
| Област≠ | Статистика | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1980 | 1978 |
| Създател≠ | Peter McCullagh | Koenker & Bassett |
| Тип≠ | Ordinal regression / GLM | Conditional quantile regression |
| Основополагащ източник≠ | 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 ↗ |
| Други названия≠ | proportional-odds model, cumulative link model, ordered logit, OLR | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Свързани≠ | 6 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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