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| Регресия на Поасон и отрицателна биномна регресия× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1998 | 2019 |
| Създател≠ | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Generalized linear model for count data | Linear regression |
| Основополагащ източник≠ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Други названия | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани≠ | 4 | 5 |
| Резюме≠ | Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred. | 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). |
| ScholarGateНабор от данни ↗ |
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