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Robust Estimation of Mean and Dispersion Functions in Extended Generalized Additive Models
- Source :
- Biometrics. 68:31-44
- Publication Year :
- 2011
- Publisher :
- Wiley, 2011.
-
Abstract
- Generalized Linear Models are a widely used method to obtain parametric es- timates for the mean function. They have been further extended to allow the re- lationship between the mean function and the covariates to be more flexible via Generalized Additive Models. However the fixed variance structure can in many cases be too restrictive. The Extended Quasi-Likelihood (EQL) framework allows for estimation of both the mean and the dispersion/variance as functions of covari- ates. As for other maximum likelihood methods though, EQL estimates are not resistant to outliers: we need methods to obtain robust estimates for both the mean and the dispersion function. In this paper we obtain functional estimates for the mean and the dispersion that are both robust and smooth. The performance of the proposed method is illustrated via a simulation study and some real data examples.
- Subjects :
- Statistics and Probability
Generalized linear model
Quasilikelihood
P-splines
Mean regression function
Models, Biological
General Biochemistry, Genetics and Molecular Biology
Dispersion
Generalized additive modeling
M-estimation
Robust estimation
Statistics
Covariate
Applied mathematics
Computer Simulation
Statistical dispersion
dispersion
generalized additive modelling
mean regression function
quasilikelihood
robust estimation
Mathematics
Parametric statistics
Models, Statistical
General Immunology and Microbiology
Applied Mathematics
Generalized additive model
jel:C13
General Medicine
Function (mathematics)
Variance (accounting)
jel:C14
Outlier
Settore SECS-S/01 - Statistica
General Agricultural and Biological Sciences
Algorithms
Subjects
Details
- ISSN :
- 0006341X
- Volume :
- 68
- Database :
- OpenAIRE
- Journal :
- Biometrics
- Accession number :
- edsair.doi.dedup.....5d82cac7be5b2330b9200410c9024b5f