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| Null-inflationsmodell× | Survival Regression× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1992 | 1980s |
| Urheber≠ | Diane Lambert | Kalbfleisch & Prentice; Cox & Oakes |
| Typ≠ | Count regression with excess zeros | Parametric survival model |
| Wegweisende Quelle≠ | Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14. DOI ↗ | Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576 |
| Aliasnamen | ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial | accelerated failure time model, AFT model, parametric survival model, time-to-event regression |
| Verwandt≠ | 6 | 3 |
| Zusammenfassung≠ | 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. | Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood. |
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