Vertaile menetelmiä
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| Nollatäytteinen malli× | Poisson- ja negatiivinen binomiregressio× | |
|---|---|---|
| Tieteenala≠ | Tilastotiede | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1992 | 1998 |
| Kehittäjä≠ | Diane Lambert | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Tyyppi≠ | Count regression with excess zeros | Generalized linear model for count data |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet | ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Liittyvät≠ | 6 | 4 |
| Tiivistelmä≠ | 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. | 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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