विधियों की तुलना करें

	शून्य-स्फीत पॉइसन (ZIP) प्रतिगमन	ऋणात्मक द्विपद समाश्रयण (Negative Binomial Regression)
क्षेत्र	सांख्यिकी	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1992	2011
प्रवर्तक	Diane Lambert	Hilbe (textbook treatment); generalized linear model framework
प्रकार	Count regression (two-component mixture)	Generalized linear model for count data
मौलिक स्रोत	Lambert, D. (1992). Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics, 34(1), 1–14.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
उपनाम	ZIP regression, zero-inflated count model, Sıfır-Şişirilmiş Poisson Regresyonu (ZIP)	NB regression, NB2 regression, negatif binom regresyonu
संबंधित	4	4
सारांश	Zero-Inflated Poisson regression is a two-component model for count data that contains more zeros than an ordinary Poisson model can explain. Introduced by Diane Lambert in 1992, it combines a logistic model for the zero-generating mechanism with a Poisson model for the genuine counting process.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.