विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| द्विचर प्रोबिट मॉडल× | ऑर्डर्ड लॉजिस्टिक रिग्रेशन (ऑर्डर्ड लॉजिट/प्रोबिट)× | प्रॉबिट रिग्रेशन मॉडल× | |
|---|---|---|---|
| क्षेत्र | अर्थमिति | अर्थमिति | अर्थमिति |
| परिवार | Regression model | Regression model | Regression model |
| उद्भव वर्ष≠ | 1970 | 1980 | 2018 |
| प्रवर्तक≠ | J. R. Ashford & R. R. Sowden | McCullagh (proportional odds / cumulative model) | Greene (textbook treatment); classical discrete-choice modelling |
| प्रकार≠ | Maximum-likelihood binary outcome model | Cumulative ordinal regression | Binary discrete-choice model |
| मौलिक स्रोत≠ | Ashford, J. R., & Sowden, R. R. (1970). Multi-variate probit analysis. Biometrics, 26(3), 535–546. DOI ↗ | 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 |
| उपनाम≠ | Bivariate Binary Probit, Joint Probit Model, Two-Equation Probit, İki Değişkenli Probit | ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit | probit regression, normit model, Probit Modeli |
| संबंधित≠ | 3 | 4 | 5 |
| सारांश≠ | The Bivariate Probit Model, introduced by Ashford and Sowden (1970), jointly estimates two binary outcome equations whose error terms are allowed to be correlated. By modeling both outcomes simultaneously under a bivariate normal distribution, it corrects for the dependence between decisions that separate probit regressions would ignore, producing consistent and efficient parameter estimates for researchers studying interrelated binary choices. | 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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