手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| ARIMA（自己回帰和分移動平均）モデル× | ポアソン回帰と負の二項回帰× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2015 | 1998 |
| 提唱者≠ | Box & Jenkins (Box-Jenkins methodology) | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| 種類≠ | Univariate time-series model | Generalized linear model for count data |
| 原典≠ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| 別名≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| 関連≠ | 5 | 4 |
| 概要≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | 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. |
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