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Título:
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Divergence-based estimation and testing with misclassified data
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Autores:
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Landaburu Jiménez, María Elena ;
Morales González, Domingo ;
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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Springer Verlag, 2005-07
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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/restrictedAccess
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Idiomas:
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Palabras clave:
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Estado = Publicado
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Materia = Ciencias: Estadística: Muestreo (Estadística)
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Tipo = Artículo
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Resumen:
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The well-known chi-squared goodness-of-fit test for a multinomial distribution is generally biased when the observations are subject to misclassification. In Pardo and Zografos (2000) the problem was considered using a double sampling scheme and phi-divergence test statistics. A new problem appears if the null hypothesis is not simple because it is necessary to give estimators for the unknown parameters. In this paper the minimum phi-divergence estimators are considered and some of their properties are established. The proposed phi-divergence test statistics are obtained by calculating phi-divergences between probability density functions and by replacing parameters by their minimum phi-divergence estimators in the derived expressions. Asymptotic distributions of the new test statistics are also obtained. The testing procedure is illustrated with an example
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En línea:
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https://eprints.ucm.es/id/eprint/16459/1/Landaburu02.pdf
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