विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| द्विचर प्रोबिट मॉडल× | बहुपदीय लॉजिस्टिक प्रतिगमन× | |
|---|---|---|
| क्षेत्र | अर्थमिति | अर्थमिति |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 1970 | 1974 |
| प्रवर्तक≠ | J. R. Ashford & R. R. Sowden | McFadden |
| प्रकार≠ | Maximum-likelihood binary outcome model | Multinomial logistic regression |
| मौलिक स्रोत≠ | Ashford, J. R., & Sowden, R. R. (1970). Multi-variate probit analysis. Biometrics, 26(3), 535–546. 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 |
| उपनाम | Bivariate Binary Probit, Joint Probit Model, Two-Equation Probit, İki Değişkenli Probit | multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon |
| संबंधित≠ | 3 | 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. | 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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