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| Bayesiläinen nollainflaatiomalli× | Nollatäytteinen malli× | |
|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1992–2006 | 1992 |
| Kehittäjä≠ | Lambert (1992) for ZIP; Bayesian extension by Ghosh, Mukhopadhyay & Lu (2006) | Diane Lambert |
| Tyyppi≠ | Bayesian count regression | Count regression with excess zeros |
| Alkuperäislähde≠ | Ghosh, S. K., Mukhopadhyay, P., & Lu, J.-C. (2006). Bayesian analysis of zero-inflated regression models. Journal of Statistical Planning and Inference, 136(4), 1360–1375. DOI ↗ | Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14. DOI ↗ |
| Rinnakkaisnimet | Bayesian ZIP, Bayesian ZINB, Bayesian zero-inflated Poisson, Bayesian zero-inflated negative binomial | ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial |
| Liittyvät≠ | 5 | 6 |
| Tiivistelmä≠ | The Bayesian zero-inflated model handles count data with excess zeros by combining a binary component — identifying structural zeros — with a count component (Poisson or negative binomial) for the remaining counts. Bayesian inference via MCMC provides full posterior distributions for all parameters, enabling principled uncertainty quantification and regularisation through priors. | 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. |
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