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Divergence-based estimation and testing with misclassified data

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Landaburu Jiménez, María Elena y Morales González, Domingo y Pardo Llorente, Leandro (2005) Divergence-based estimation and testing with misclassified data. Statistical Papers, 46 (3). pp. 397-409. ISSN 0932-5026

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URL Oficial: http://www.springerlink.com/content/j151063g0r87lg10/fulltext.pdf


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Resumen

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


Tipo de documento:Artículo
Palabras clave:Misclassification; Double sampling; Divergence estimators; Goodness-of-fit tests; Divergence statistics
Materias:Ciencias > Estadística > Muestreo (Estadística)
Código ID:16459
Depositado:20 Sep 2012 09:05
Última Modificación:07 Feb 2014 09:29

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