Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Järjestysregressio (järjestyslogit/probit)× | Logistinen regressio× | Multinomiaalinen logistinen regressio× | |
|---|---|---|---|
| Tieteenala≠ | Ekonometria | Tutkimuksen tilastomenetelmät | Ekonometria |
| Menetelmäperhe≠ | Regression model | Process / pipeline | Regression model |
| Syntyvuosi≠ | 1980 | 1958 | 1974 |
| Kehittäjä≠ | McCullagh (proportional odds / cumulative model) | David Roxbee Cox | McFadden |
| Tyyppi≠ | Cumulative ordinal regression | Method | Multinomial logistic regression |
| Alkuperäislähde≠ | McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. 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 |
| Rinnakkaisnimet≠ | ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit | logit model, binomial logistic regression, LR | multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon |
| Liittyvät≠ | 4 | 3 | 5 |
| 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. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. | 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. |
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