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Robust fitting of mixtures of GLMs by weighted likelihood
- Source :
- Advances in Statistical Analysis
- Publication Year :
- 2021
- Publisher :
- Springer Berlin Heidelberg, 2021.
-
Abstract
- Finite mixtures of generalized linear models are commonly fitted by maximum likelihood and the EM algorithm. The estimation process and subsequent inferential and classification procedures can be badly affected by the occurrence of outliers. Actually, contamination in the sample at hand may lead to severely biased fitted components and poor classification accuracy. In order to take into account the potential presence of outliers, a robust fitting strategy is proposed that is based on the weighted likelihood methodology. The technique exhibits a satisfactory behavior in terms of both fitting and classification accuracy, as confirmed by some numerical studies and real data examples.
- Subjects :
- 0106 biological sciences
Statistics and Probability
Generalized linear model
Economics and Econometrics
Maximum likelihood
Sample (statistics)
MSC 62H30
010603 evolutionary biology
01 natural sciences
010104 statistics & probability
MSC 62H25
Weighted likelihood
Expectation–maximization algorithm
Mixture
Outliers
0101 mathematics
Mathematics
Original Paper
Applied Mathematics
Classification
EM
Modeling and Simulation
Outlier
MSC 62G35
MSC 62F35
GLM
Algorithm
Social Sciences (miscellaneous)
Analysis
Subjects
Details
- Language :
- English
- ISSN :
- 1863818X and 18638171
- Database :
- OpenAIRE
- Journal :
- Advances in Statistical Analysis
- Accession number :
- edsair.doi.dedup.....e498bf20b3c7c6a79d52519c1e6869f1