Goodness of fit tests with misclassified data

K. F. Cheng, H. M. Hsueh, T. H. Chien

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


The most popular goodness of fit test for a multinomial distribution is the chi-square test. But this test is generally biased if observations are subject to misclassification. In this paper we shall discuss how to define a new test procedure when we have double sample data obtained from the true and fallible devices. An adjusted chi-square test based on the imputation method and the likelihood ratio test are considered. Asymptotically, these two procedures are equivalent. However, an example and simulation results show that the former procedure is not only computationally simpler but also more powerful under finite sample situations.

Original languageEnglish
Pages (from-to)1379-1393
Number of pages15
JournalCommunications in Statistics - Theory and Methods
Issue number6
Publication statusPublished - 1998
Externally publishedYes


  • Chi-square test
  • Double sampling
  • Goodness of fit
  • Imputation
  • Likelihood ratio test
  • Misclassification

ASJC Scopus subject areas

  • Statistics and Probability


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