1. Divergence-based estimation and testing with misclassified data
- Author
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Landaburu Jiménez, María Elena, Morales González, Domingo, Pardo Llorente, Leandro, Landaburu Jiménez, María Elena, Morales González, Domingo, and Pardo Llorente, Leandro
- 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, Depto. de Estadística e Investigación Operativa, Fac. de Ciencias Matemáticas, TRUE, pub
- Published
- 2023