Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Järjestysregressio (järjestyslogit/probit)× | Multinomiaalinen logistinen regressio× | Negatiivinen binomiregressio× | |
|---|---|---|---|
| Tieteenala | Ekonometria | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model | Regression model |
| Syntyvuosi≠ | 1980 | 1974 | 2011 |
| Kehittäjä≠ | McCullagh (proportional odds / cumulative model) | McFadden | Hilbe (textbook treatment); generalized linear model framework |
| Tyyppi≠ | Cumulative ordinal regression | Multinomial logistic regression | Generalized linear model for count data |
| Alkuperäislähde≠ | McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142. DOI ↗ | McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503 | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ |
| Rinnakkaisnimet≠ | ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit | multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon | NB regression, NB2 regression, negatif binom regresyonu |
| Liittyvät≠ | 4 | 5 | 4 |
| Tiivistelmä≠ | 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. | Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category. | 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. |
| ScholarGateAineisto ↗ |
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