Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Упорядоченная логистическая регрессия (Ordered Logit/Probit)× | Регрессия отрицательного биномиального распределения× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1980 | 2011 |
| Автор метода≠ | McCullagh (proportional odds / cumulative model) | Hilbe (textbook treatment); generalized linear model framework |
| Тип≠ | Cumulative ordinal regression | Generalized linear model for count data |
| Основополагающий источник≠ | McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142. DOI ↗ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ |
| Другие названия≠ | ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit | NB regression, NB2 regression, negatif binom regresyonu |
| Связанные | 4 | 4 |
| Сводка≠ | Ordered logit is a cumulative regression model for an ordinal dependent variable, fitting a logit (or probit) link to the cumulative category probabilities. Developed in McCullagh's 1980 treatment of regression models for ordinal data, it is the standard tool for Likert-scale, rating, and ranked outcomes. | Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data. |
| ScholarGateНабор данных ↗ |
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