Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robusts nulles piepildītais modelis× | Robusts vispārinātais lineārais modelis× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1990s–2000s | 2001 |
| Autors≠ | Extension of Lambert (1992) ZIP model combined with robust M-estimation and sandwich standard errors | Cantoni & Ronchetti |
| Tips≠ | Robust count regression with excess zeros | Robust regression model |
| Pirmavots≠ | Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8), 1–25. DOI ↗ | Heritier, S., Cantoni, E., Copt, S., & Victoria-Feser, M.-P. (2009). Robust Methods in Biostatistics. Wiley. ISBN: 978-0470027264 |
| Citi nosaukumi | robust ZIP, robust ZINB, outlier-resistant zero-inflated regression, robust zero-inflated Poisson | robust GLM, GLM with robust estimation, robust quasi-likelihood model, M-estimator GLM |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The robust zero-inflated model extends standard zero-inflated count regression — which handles excess zeros via a mixture of a point mass at zero and a count distribution — by replacing or supplementing classical maximum likelihood with robust estimation techniques (M-estimators, sandwich standard errors) that protect against the distorting influence of outlying observations. | A Robust Generalized Linear Model fits the standard GLM family — linear, logistic, Poisson, and others — using M-type estimating equations that down-weight outlying or influential observations. The result is coefficient estimates and standard errors that remain stable even when a minority of data points deviate sharply from the assumed distribution. |
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