Σύγκριση μεθόδων

	Εύρωστη Παλινδρόμηση Poisson	Λογιστική Παλινδρόμηση
Πεδίο	Στατιστική	Ερευνητική Στατιστική
Οικογένεια	Regression model	Process / pipeline
Έτος προέλευσης	2004	1958
Δημιουργός	Guangyong Zou	David Roxbee Cox
Τύπος	GLM with robust variance	Method
Θεμελιώδης πηγή	Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Εναλλακτικές ονομασίες	modified Poisson regression, Poisson regression with robust standard errors, log-binomial alternative, sandwich-variance Poisson	logit model, binomial logistic regression, LR
Συναφείς	5	3
Σύνοψη	Robust Poisson regression fits a Poisson log-linear model to a binary outcome but replaces the model-based variance with the empirical sandwich estimator. This yields valid standard errors and risk ratios even though Poisson variance assumptions are technically violated for binary data. The approach, popularized by Zou (2004), is widely used in epidemiology as a numerically stable alternative to log-binomial regression.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.