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Título:
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Generalized Wald-type tests based on minimum density power divergence estimators
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Autores:
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Basu, A. ;
Mandal, A. ;
Martin, N. ;
Pardo Llorente, Leandro
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Tipo de documento:
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texto impreso
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Editorial:
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Taylor & Francis Group Ltd, 2015
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Dimensiones:
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application/pdf
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Nota general:
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info:eu-repo/semantics/openAccess
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Idiomas:
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Palabras clave:
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Estado = En prensa
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Materia = Ciencias: Matemáticas: Estadística matemática
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Tipo = Artículo
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Resumen:
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In testing of hypothesis, the robustness of the tests is an important concern. Generally, the maximum likelihood-based tests are most efficient under standard regularity conditions, but they are highly non-robust even under small deviations from the assumed conditions. In this paper, we have proposed generalized Wald-type tests based on minimum density power divergence estimators for parametric hypotheses. This method avoids the use of nonparametric density estimation and the bandwidth selection. The trade-off between efficiency and robustness is controlled by a tuning parameter ?. The asymptotic distributions of the test statistics are chi-square with appropriate degrees of freedom. The performance of the proposed tests is explored through simulations and real data analysis
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En línea:
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https://eprints.ucm.es/29619/1/1403.7616v3.pdf
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