A robust conditional maximum likelihood estimator for generalized linear models with a dispersion parameter
Highly robust and efficient estimators for generalized linear models with a dispersion parameter are proposed. The estimators are based on three steps. In the first step, the maximum rank correlation estimator is used to consistently estimate the slopes up to a scale factor. The scale factor, the in...
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Springer New York LLC
2019
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
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| LEADER | 09097caa a22008297a 4500 | ||
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| 001 | PAPER-25626 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518205742.0 | ||
| 008 | 190410s2019 xx ||||fo|||| 00| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-85058084037 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 100 | 1 | |a Marazzi, A. | |
| 245 | 1 | 2 | |a A robust conditional maximum likelihood estimator for generalized linear models with a dispersion parameter |
| 260 | |b Springer New York LLC |c 2019 | ||
| 270 | 1 | 0 | |m Marazzi, A.; Institute of Social and Preventive MedicineSwitzerland; email: alfio.marazzi@chuv.ch |
| 506 | |2 openaire |e Política editorial | ||
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| 520 | 3 | |a Highly robust and efficient estimators for generalized linear models with a dispersion parameter are proposed. The estimators are based on three steps. In the first step, the maximum rank correlation estimator is used to consistently estimate the slopes up to a scale factor. The scale factor, the intercept, and the dispersion parameter are robustly estimated using a simple regression model. Then, randomized quantile residuals based on the initial estimators are used to define a region S such that observations out of S are considered as outliers. Finally, a conditional maximum likelihood (CML) estimator given the observations in S is computed. We show that, under the model, S tends to the whole space for increasing sample size. Therefore, the CML estimator tends to the unconditional maximum likelihood estimator and this implies that this estimator is asymptotically fully efficient. Moreover, the CML estimator maintains the high degree of robustness of the initial one. The negative binomial regression case is studied in detail. © 2018, Sociedad de Estadística e Investigación Operativa. |l eng | |
| 536 | |a Detalles de la financiación: Victor Yohai was partially supported by grants pict 2014-0351 from anpcyt and 20020130100279BA from the Universidad de Buenos Aires at Buenos Aires, Argentina. Marina Valdora was partially supported by grant 20020130100279Ba from the Universidad de Buenos Aires at Buenos Aires, Argentina. We also thank two anonymous referees for their valuable comments that helped to improve the presentation of the paper. | ||
| 593 | |a Institute of Social and Preventive Medicine, Lausanne, Switzerland | ||
| 593 | |a Nice Computing, Le Mont-sur-Lausanne, Switzerland | ||
| 593 | |a Departamento de matematicas and Instituto de cálculo, Facultad de ciencias exactas y naturales, Universidad de Buenos Aires, Buenos Aires, Argentina | ||
| 593 | |a CONICET, Buenos Aires, Argentina | ||
| 690 | 1 | 0 | |a CONDITIONAL MAXIMUM LIKELIHOOD |
| 690 | 1 | 0 | |a GENERALIZED LINEAR MODEL |
| 690 | 1 | 0 | |a NEGATIVE BINOMIAL REGRESSION |
| 690 | 1 | 0 | |a OVERDISPERSION |
| 690 | 1 | 0 | |a ROBUST REGRESSION |
| 700 | 1 | |a Valdora, M. | |
| 700 | 1 | |a Yohai, V. | |
| 700 | 1 | |a Amiguet, M. | |
| 773 | 0 | |d Springer New York LLC, 2019 |g v. 28 |h pp. 223-241 |k n. 1 |p Test |x 11330686 |t Test | |
| 856 | 4 | 1 | |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058084037&doi=10.1007%2fs11749-018-0624-0&partnerID=40&md5=9aac02a917ce3e188a4a981fda524c60 |y Registro en Scopus |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s11749-018-0624-0 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_11330686_v28_n1_p223_Marazzi |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_11330686_v28_n1_p223_Marazzi |y Registro en la Biblioteca Digital |
| 961 | |a paper_11330686_v28_n1_p223_Marazzi |b paper |c PE | ||
| 962 | |a info:eu-repo/semantics/article |a info:ar-repo/semantics/artículo |b info:eu-repo/semantics/publishedVersion | ||
| 999 | |c 86579 | ||