Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Подредена логистична регресия (Ordered Logit/Probit)× | Негативно-биномна регресия× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1980 | 2011 |
| Създател≠ | McCullagh (proportional odds / cumulative model) | Hilbe (textbook treatment); generalized linear model framework |
| Тип≠ | Cumulative ordinal regression | Generalized linear model for count data |
| Основополагащ източник≠ | 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 ↗ |
| Други названия≠ | ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit | NB regression, NB2 regression, negatif binom regresyonu |
| Свързани | 4 | 4 |
| Резюме≠ | 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 от данни ↗ |
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