Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi wa Poisson wenye Mielekeo Mikuu ya Sifuri (ZIP)× | Rega ya Kina cha Zero (ZINB)× | |
|---|---|---|
| Nyanja | Takwimu | Takwimu |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1992 | 1994 |
| Mwanzilishi≠ | Diane Lambert | Greene (1994) |
| Aina≠ | Count regression (two-component mixture) | Count regression (mixture model) |
| Chanzo asilia≠ | Lambert, D. (1992). Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics, 34(1), 1–14. DOI ↗ | Greene, W. H. (1994). Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models. NYU Working Paper. link ↗ |
| Majina mbadala≠ | ZIP regression, zero-inflated count model, Sıfır-Şişirilmiş Poisson Regresyonu (ZIP) | ZINB, ZINB regression, zero-inflated negative binomial model, Sıfır-Şişirilmiş Negatif Binom Regresyonu (ZINB) |
| Zinazohusiana≠ | 4 | 5 |
| 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. | Zero-Inflated Negative Binomial regression is a count model, introduced by Greene (1994), that handles count data showing both an excess of zeros and overdispersion. It combines a binary inflation process that generates structural zeros with a negative binomial count process, making it one of the most widely used distributions for real-world count data. |
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