Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Secvādā loģistikas regresija (secīgs logit/probit)× | Probit regresijas modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1980 | 2018 |
| Autors≠ | McCullagh (proportional odds / cumulative model) | Greene (textbook treatment); classical discrete-choice modelling |
| Tips≠ | Cumulative ordinal regression | Binary discrete-choice model |
| Pirmavots≠ | McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142. DOI ↗ | Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366 |
| Citi nosaukumi≠ | ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit | probit regression, normit model, Probit Modeli |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | 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. | The probit model is a regression method for a binary (0/1) outcome that maps a linear index of the predictors through the standard normal cumulative distribution function to produce a probability. It is a classical discrete-choice alternative to logistic regression, developed in standard econometrics treatments such as Greene's Econometric Analysis (2018). |
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