Σύγκριση μεθόδων

	Λογιστική Παλινδρόμηση Διατακτικής Κλίμακας (Μοντέλο Αναλογικών Πιθανοτήτων)	Παλινδρόμηση Poisson και Αρνητική Διωνυμική
Πεδίο	Στατιστική	Οικονομετρία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2010	1998
Δημιουργός	Agresti (textbook treatment); proportional odds model	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Τύπος	Ordinal logistic regression	Generalized linear model for count data
Θεμελιώδης πηγή	Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Εναλλακτικές ονομασίες	proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds)	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Συναφείς	5	4
Σύνοψη	Ordinal logistic regression models an ordered categorical outcome — such as a Likert rating, a satisfaction level, or an education tier — as a function of predictors. It is the ordinal extension of logistic regression, developed in standard treatments such as Agresti's Analysis of Ordinal Categorical Data (2010), and in its most common form it is the proportional odds model.	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.