Sammenlign metoder

	Robust generaliseret lineær model	Robust logistisk regression
Fagområde	Statistik	Statistik
Familie	Regression model	Regression model
Oprindelsesår	2001	2001
Ophavsperson	Cantoni & Ronchetti	Cantoni & Ronchetti (2001); Bondell (2008)
Type	Robust regression model	Robust generalized linear model (binary outcome)
Oprindelig kilde	Heritier, S., Cantoni, E., Copt, S., & Victoria-Feser, M.-P. (2009). Robust Methods in Biostatistics. Wiley. ISBN: 978-0470027264	Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030.
Aliasser	robust GLM, GLM with robust estimation, robust quasi-likelihood model, M-estimator GLM	robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon
Relaterede	5	5
Resumé	A Robust Generalized Linear Model fits the standard GLM family — linear, logistic, Poisson, and others — using M-type estimating equations that down-weight outlying or influential observations. The result is coefficient estimates and standard errors that remain stable even when a minority of data points deviate sharply from the assumed distribution.	Robust Logistic Regression is a variant of logistic regression that is resistant to outliers and leverage points, fitting a binary or categorical outcome with Mallows-type weighted estimation. The robust framework for generalized linear models was developed by Cantoni and Ronchetti (2001), with a weighting approach later refined by Bondell (2008).