เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การถดถอยโลจิสติกส์หลายตัวแปร (Multinomial Logistic Regression)× | การถดถอยโลจิสติกอันดับ (Ordered Logit/Probit)× | แบบจำลองโพรบิต (Probit Regression Model)× | |
|---|---|---|---|
| สาขาวิชา | เศรษฐมิติ | เศรษฐมิติ | เศรษฐมิติ |
| ตระกูล | Regression model | Regression model | Regression model |
| ปีกำเนิด≠ | 1974 | 1980 | 2018 |
| ผู้ริเริ่ม≠ | McFadden | McCullagh (proportional odds / cumulative model) | Greene (textbook treatment); classical discrete-choice modelling |
| ประเภท≠ | Multinomial logistic regression | Cumulative ordinal regression | Binary discrete-choice model |
| แหล่งต้นตำรับ≠ | 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 | 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 |
| ชื่อเรียกอื่น≠ | multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon | ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit | probit regression, normit model, Probit Modeli |
| ที่เกี่ยวข้อง≠ | 5 | 4 | 5 |
| สรุป≠ | 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. | 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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