Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robusts vispārinātais lineārais modelis× | Vispārīgais lineārais modelis (GLM)× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2001 | 1972 |
| Autors≠ | Cantoni & Ronchetti | John A. Nelder & Robert W. M. Wedderburn |
| Tips≠ | Robust regression model | Regression framework |
| Pirmavots≠ | Heritier, S., Cantoni, E., Copt, S., & Victoria-Feser, M.-P. (2009). Robust Methods in Biostatistics. Wiley. ISBN: 978-0470027264 | Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384. DOI ↗ |
| Citi nosaukumi | robust GLM, GLM with robust estimation, robust quasi-likelihood model, M-estimator GLM | GLM, generalized regression, exponential family regression, link-function model |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | 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. | The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case. |
| ScholarGateDatu kopa ↗ |
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