विधियों की तुलना करें

	ऑर्डर्ड लॉजिस्टिक रिग्रेशन (ऑर्डर्ड लॉजिट/प्रोबिट)	ऋणात्मक द्विपद समाश्रयण (Negative Binomial Regression)
क्षेत्र	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1980	2011
प्रवर्तक	McCullagh (proportional odds / cumulative model)	Hilbe (textbook treatment); generalized linear model framework
प्रकार	Cumulative ordinal regression	Generalized linear model for count data
मौलिक स्रोत	McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
उपनाम	ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit	NB regression, NB2 regression, negatif binom regresyonu
संबंधित	4	4
सारांश	Ordered logit is a cumulative regression model for an ordinal dependent variable, fitting a logit (or probit) link to the cumulative category probabilities. Developed in McCullagh's 1980 treatment of regression models for ordinal data, it is the standard tool for Likert-scale, rating, and ranked outcomes.	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.