ScholarGate
Asistent

Usporedite metode

Pregledajte odabrane metode jednu uz drugu; retci koji se razlikuju su istaknuti.

Robusna Poissonova regresija×Logistička regresija×
PodručjeStatistikaIstraživačka statistika
ObiteljRegression modelProcess / pipeline
Godina nastanka20041958
TvoracGuangyong ZouDavid Roxbee Cox
VrstaGLM with robust varianceMethod
Temeljni izvorZou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706. DOI ↗Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗
Drugi nazivimodified Poisson regression, Poisson regression with robust standard errors, log-binomial alternative, sandwich-variance Poissonlogit model, binomial logistic regression, LR
Srodne53
SažetakRobust 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.
ScholarGateSkup podataka
  1. v1
  2. 2 Izvori
  3. PUBLISHED
  1. v1
  2. 2 Izvori
  3. PUBLISHED

Idi na pretraživanje Preuzmi prezentaciju

ScholarGateUsporedite metode: Robust Poisson Regression · Logistic Regression. Preuzeto 2026-06-18 s https://scholargate.app/hr/compare