방법 비교

	강건 영과대 모형	Zero-Inflated Model
분야	통계학	통계학
계열	Regression model	Regression model
기원 연도	1990s–2000s	1992
창시자	Extension of Lambert (1992) ZIP model combined with robust M-estimation and sandwich standard errors	Diane Lambert
유형	Robust count regression with excess zeros	Count regression with excess zeros
원전	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.
별칭	robust ZIP, robust ZINB, outlier-resistant zero-inflated regression, robust zero-inflated Poisson	ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial
관련	5	6
요약	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.