Robust inference on the parameters in generalized linear models is performed using the weighted likelihood method. Two cases are considered: a case with replicated observations and a case with a single observation of the dependent variable for each combination of the explanatory variables. The first case is common in the design of experiments, while the second case arises in observational studies. Theoretical and computational results on real datasets are presented and compared with other existing techniques.
Robust inference in generalized linear models / Alqallaf, Fatemah; Agostinelli, Claudio. - In: COMMUNICATIONS IN STATISTICS. SIMULATION AND COMPUTATION. - ISSN 0361-0918. - STAMPA. - 2016, 45:9(2016), pp. 3053-3073. [10.1080/03610918.2014.911896]
Robust inference in generalized linear models
Agostinelli, Claudio
2016-01-01
Abstract
Robust inference on the parameters in generalized linear models is performed using the weighted likelihood method. Two cases are considered: a case with replicated observations and a case with a single observation of the dependent variable for each combination of the explanatory variables. The first case is common in the design of experiments, while the second case arises in observational studies. Theoretical and computational results on real datasets are presented and compared with other existing techniques.File | Dimensione | Formato | |
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Robust Inference in Generalized Linear Models.pdf
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