Statistical Papers

, Volume 46, Issue 3, pp 397–409

Divergence-based estimation and testing with misclassified data

  • E. Landaburu
  • D. Morales
  • L. Pardo

DOI: 10.1007/BF02762841

Cite this article as:
Landaburu, E., Morales, D. & Pardo, L. Statistical Papers (2005) 46: 397. doi:10.1007/BF02762841


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 ø-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 ø-divergence estimators are considered and some of their properties are established. The proposed ø-divergence test statistics are obtained by calculating ø-divergences between probability density functions and by replacing parameters by their minimum ø-divergence estimators in the derived expressions. Asymptotic distributions of the new test statistics are also obtained. The testing procedure is illustrated with an example.

Key words and phrases

Misclassification Double sampling Divergence estimators Goodness-of-fit tests Divergence statistics 

AMS Classification

62F05 62B10 

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • E. Landaburu
    • 1
  • D. Morales
    • 2
  • L. Pardo
    • 1
  1. 1.Department of Statistics & O. R.Complutense University of MadridMadrid
  2. 2.Operations Research CenterMiguel Hernández University of ElcheElche

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