Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Ординальная логистическая регрессия× | Мультиномиальная логистическая регрессия× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1980 | 1966–1974 |
| Автор метода≠ | Peter McCullagh | Cox (1966); Theil (1969); formalized by McFadden (1974) |
| Тип≠ | Ordinal regression / GLM | Generalized linear model |
| Основополагающий источник≠ | McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142. DOI ↗ | Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933 |
| Другие названия | proportional-odds model, cumulative link model, ordered logit, OLR | polytomous logistic regression, softmax regression, multinomial logit, nominal logistic regression |
| Связанные≠ | 6 | 4 |
| Сводка≠ | 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. | Multinomial logistic regression extends binary logistic regression to outcomes with three or more unordered categories. It models the log-odds of each category relative to a chosen reference category as a linear function of the predictors, and estimates all parameters simultaneously via maximum likelihood. It is the standard choice when the dependent variable is nominal with multiple levels. |
| ScholarGateНабор данных ↗ |
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