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DziedzinaStatystykaEkonometria
RodzinaRegression modelRegression model
Rok powstania19801978
TwórcaPeter McCullaghKoenker & Bassett
TypOrdinal regression / GLMConditional quantile regression
Źródło pierwotneMcCullagh, 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 ↗
Inne nazwyproportional-odds model, cumulative link model, ordered logit, OLRconditional quantile regression, regression quantiles, Kantil Regresyon
Pokrewne65
PodsumowanieOrdinal 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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  3. PUBLISHED

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ScholarGatePorównaj metody: Ordinal Logistic Regression · Quantile Regression. Pobrano 2026-06-17 z https://scholargate.app/pl/compare