.png){kind=icon}
.png){kind=icon}
Return
Robust inference for generalized linear models
.png){kind=icon}
OA
AI
DOI：10.1198/016214501753209004.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
By starting from a natural class of robust estimators for generalized linear models based on the notion of qua-si-likelihood, we define robust deviances that can be used for stepwise model selection as in the classical framework. Wc derive the asymptotic distribution of tests based on robust deviances, and we investigate the stability of their asymptotic level under contamination. The binomial and Poisson models are treated in detail. Two applications to real data and a sensitivity analysis show that the inference obtained by means of the new techniques is more reliable than that obtained by classical estimation and testing procedures.
Keywords:
binomial regression
influence function
M-estimators
model selections
Poisson regression
quasi-likehood
robust deviance
robustness of efficiency
robustness of validity
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3
Papers:
5.1K
Citations:
4.8W
Organization
No organization information available