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	पॉइसन और ऋणात्मक द्विपद प्रतिगमन (Poisson and Negative Binomial Regression)	क्वांटाइल रिग्रेशन
क्षेत्र	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1998	1978
प्रवर्तक	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)	Koenker & Bassett
प्रकार	Generalized linear model for count data	Conditional quantile regression
मौलिक स्रोत	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
उपनाम	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon	conditional quantile regression, regression quantiles, Kantil Regresyon
संबंधित	4	5
सारांश	Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.