Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Modeli wenye kuongezeka sifuri (Zero-Inflated Model)× | Usuli wa Regresi ya Binomiali Hasiri× | |
|---|---|---|
| Nyanja≠ | Takwimu | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1992 | 2011 |
| Mwanzilishi≠ | Diane Lambert | Hilbe (textbook treatment); generalized linear model framework |
| Aina≠ | Count regression with excess zeros | 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 ↗ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ |
| Majina mbadala≠ | ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial | NB regression, NB2 regression, negatif binom regresyonu |
| Zinazohusiana≠ | 6 | 4 |
| Muhtasari≠ | A zero-inflated model is a two-component mixture regression designed for count outcomes that contain more zero values than a standard Poisson or negative binomial distribution can accommodate. One component is a binary process that generates structural zeros; the other is a count process that generates both zeros and positive counts. | Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data. |
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