Salīdzināt metodes

	Robusts nulles piepildītais modelis	Modelis ar pārmērīgu nulles vērtību skaitu
Nozare	Statistika	Statistika
Saime	Regression model	Regression model
Izcelsmes gads	1990s–2000s	1992
Autors	Extension of Lambert (1992) ZIP model combined with robust M-estimation and sandwich standard errors	Diane Lambert
Tips	Robust count regression with excess zeros	Count regression with excess zeros
Pirmavots	Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8), 1–25.	Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14.
Citi nosaukumi	robust ZIP, robust ZINB, outlier-resistant zero-inflated regression, robust zero-inflated Poisson	ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial
Saistītās	5	6
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 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.