Landaburu Jiménez, María Elena and Morales González, Domingo and 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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Official URL: http://www.springerlink.com/content/j151063g0r87lg10/fulltext.pdf

## Abstract

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

Item Type: | Article |
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Uncontrolled Keywords: | Misclassification; Double sampling; Divergence estimators; Goodness-of-fit tests; Divergence statistics |

Subjects: | Sciences > Statistics > Sampling (Statistics) |

ID Code: | 16459 |

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Deposited On: | 20 Sep 2012 09:05 |

Last Modified: | 07 Feb 2014 09:29 |

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