方法对比
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| 泊松回归与负二项回归× | Theta 方法× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1998 | 2000 |
| 提出者≠ | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) | Assimakopoulos & Nikolopoulos |
| 类型≠ | Generalized linear model for count data | Univariate time-series forecasting model |
| 开创性文献≠ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ | Assimakopoulos, V. & Nikolopoulos, K. (2000). The Theta Model: A Decomposition Approach to Forecasting. International Journal of Forecasting, 16(4), 521-530. DOI ↗ |
| 别名≠ | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon | theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi |
| 相关 | 4 | 4 |
| 摘要≠ | 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. | The Theta Method is a univariate time-series forecasting model introduced by Assimakopoulos and Nikolopoulos in 2000. It decomposes a series into two theta lines that capture its long-run trend and its short-run dynamics, forecasts each line separately, and combines them by a weighted average. Its simplicity and accuracy made it the winner of the M3 forecasting competition. |
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