
A closed-form alternative estimator for GLM with categorical explanatory variables
Brouste, Alexandre; Dutang, Christophe; Rohmer, Tom (2022), A closed-form alternative estimator for GLM with categorical explanatory variables, Communications in Statistics - Simulation and Computation, p. 1-17. 10.1080/03610918.2022.2076870
Type
Article accepté pour publication ou publiéDate
2022Journal name
Communications in Statistics - Simulation and ComputationPages
1-17
Publication identifier
Metadata
Show full item recordAuthor(s)
Brouste, Alexandre
Laboratoire Manceau de Mathématiques [LMM]
Dutang, Christophe

CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Rohmer, Tom
Génétique Physiologie et Systèmes d'Elevage [GenPhySE ]
Abstract (EN)
The parameters of generalized linear models (GLMs) are usually estimated by the maximum likelihood estimator (MLE) which is known to be asymptotically efficient. But the MLE is computed using a Newton-Raphson-type algorithm which is time-consuming for a large number of variables or modalities, or a large sample size. An alternative closed-form estimator is proposed in this paper in the case of categorical explanatory variables. Asymptotic properties of the alternative estimator is studied. The performances in terms of both computation time and asymptotic variance of the proposed estimator are compared with the MLE for a Gamma distributed GLM.Subjects / Keywords
Regression models; explicit estimators; categorical explanatory variables; GLM; asymptotic distributionRelated items
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