方法对比
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| 多项逻辑回归× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1974 | 2019 |
| 提出者≠ | McFadden | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Multinomial logistic regression | Linear regression |
| 开创性文献≠ | 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 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名 | multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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