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المؤلفون: Graciela Boente, Isabel M. Rodrigues, Ana M. Bianco
المصدر: CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICETمصطلحات موضوعية: Generalized linear model, Statistics and Probability, Numerical Analysis, OUTLIERS, Estadística y Probabilidad, Matemáticas, purl.org/becyt/ford/1.1 [https], Linear model, Fisher consistency, Estimator, M-estimator, FISHER-CONSISTENCY, Missing data, Poisson distribution, purl.org/becyt/ford/1 [https], GENERALIZED LNEAR MODEL, symbols.namesake, MISSING DATA, Outlier, Statistics, symbols, Applied mathematics, Statistics, Probability and Uncertainty, CIENCIAS NATURALES Y EXACTAS, Mathematics
الوصف: When dealing with situations in which the responses are discrete or show some type of asymmetry, the linear model is not appropriate to establish the relation between the responses and the covariates. Generalized linear models serve this purpose, since they allow one to model the mean of the responses through a link function, linearly on the covariates. When atypical observations are present in the sample, robust estimators are useful to provide fair estimations as well as to build outlier detection rules. The focus of this paper is to define robust estimators for the regression parameter when missing data possibly occur in the responses. The estimators introduced turn out to be consistent under mild conditions. In particular, resistant methods for Poisson and Gamma models are given. A simulation study allows one to compare the behaviour of the classical and robust estimators, under different contamination schemes. The robustness of the proposed procedures is studied through the influence function, while asymptotic variances are derived from it. Besides, outlier detection rules are defined using the influence function. The procedure is also illustrated by analysing a real data set. Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Rodrigues, Isabel. Technical University of Lisbon; Portugal
وصف الملف: application/pdf
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::765d211672555d07cae2c60395eafae8Test
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2دورية أكاديمية
مصطلحات موضوعية: Fisher-Consistency, Generalized Lnear Model, Missing Data, Outliers, https://purl.org/becyt/ford/1.1Test, https://purl.org/becyt/ford/1Test
الوصف: When dealing with situations in which the responses are discrete or show some type of asymmetry, the linear model is not appropriate to establish the relation between the responses and the covariates. Generalized linear models serve this purpose, since they allow one to model the mean of the responses through a link function, linearly on the covariates. When atypical observations are present in the sample, robust estimators are useful to provide fair estimations as well as to build outlier detection rules. The focus of this paper is to define robust estimators for the regression parameter when missing data possibly occur in the responses. The estimators introduced turn out to be consistent under mild conditions. In particular, resistant methods for Poisson and Gamma models are given. A simulation study allows one to compare the behaviour of the classical and robust estimators, under different contamination schemes. The robustness of the proposed procedures is studied through the influence function, while asymptotic variances are derived from it. Besides, outlier detection rules are defined using the influence function. The procedure is also illustrated by analysing a real data set. ; Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina ; Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina ; Fil: Rodrigues, Isabel. Technical University of Lisbon; Portugal
وصف الملف: application/pdf
العلاقة: info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0047259X12002060Test; http://hdl.handle.net/11336/15863Test; Bianco, Ana Maria; Boente Boente, Graciela Lina; Rodrigues, Isabel; Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection; Elsevier Inc; Journal Of Multivariate Analysis; 114; 2-2013; 209-226
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3دورية أكاديمية
مصطلحات موضوعية: FISHER-CONSISTENCY, GENERALIZED LNEAR MODEL, MISSING DATA, OUTLIERS, Estadística y Probabilidad, Matemáticas, CIENCIAS NATURALES Y EXACTAS, stat
الوصف: When dealing with situations in which the responses are discrete or show some type of asymmetry, the linear model is not appropriate to establish the relation between the responses and the covariates. Generalized linear models serve this purpose, since they allow one to model the mean of the responses through a link function, linearly on the covariates. When atypical observations are present in the sample, robust estimators are useful to provide fair estimations as well as to build outlier detection rules. The focus of this paper is to define robust estimators for the regression parameter when missing data possibly occur in the responses. The estimators introduced turn out to be consistent under mild conditions. In particular, resistant methods for Poisson and Gamma models are given. A simulation study allows one to compare the behaviour of the classical and robust estimators, under different contamination schemes. The robustness of the proposed procedures is studied through the influence function, while asymptotic variances are derived from it. Besides, outlier detection rules are defined using the influence function. The procedure is also illustrated by analysing a real data set. ; Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina ; Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina ; Fil: Rodrigues, Isabel. Technical University of Lisbon; Portugal
