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。
| ゼロ過剰ポアソン（ZIP）回帰× | ロジスティック回帰× | |
|---|---|---|
| 分野≠ | 統計学 | 研究統計 |
| 系統≠ | Regression model | Process / pipeline |
| 提唱年≠ | 1992 | 1958 |
| 提唱者≠ | Diane Lambert | David Roxbee Cox |
| 種類≠ | Count regression (two-component mixture) | Method |
| 原典≠ | Lambert, D. (1992). Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics, 34(1), 1–14. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 別名 | ZIP regression, zero-inflated count model, Sıfır-Şişirilmiş Poisson Regresyonu (ZIP) | logit model, binomial logistic regression, LR |
| 関連≠ | 4 | 3 |
| 概要≠ | Zero-Inflated Poisson regression is a two-component model for count data that contains more zeros than an ordinary Poisson model can explain. Introduced by Diane Lambert in 1992, it combines a logistic model for the zero-generating mechanism with a Poisson model for the genuine counting process. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
| ScholarGateデータセット ↗ |
|
|