Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi wa Poisson wenye Mielekeo Mikuu ya Sifuri (ZIP)× | Uchanganuzi wa Poisson na Negative Binomial× | |
|---|---|---|
| Nyanja≠ | Takwimu | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1992 | 1998 |
| Mwanzilishi≠ | Diane Lambert | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Aina≠ | Count regression (two-component mixture) | Generalized linear model for count data |
| Chanzo asilia≠ | Lambert, D. (1992). Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics, 34(1), 1–14. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Majina mbadala≠ | ZIP regression, zero-inflated count model, Sıfır-Şişirilmiş Poisson Regresyonu (ZIP) | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Zinazohusiana | 4 | 4 |
| Muhtasari≠ | 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. | 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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