Testing the adequacy for a general linear errors-in-variables model

Lixing Zhu*, Hengjian Cui

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

23 Citations (Scopus)


In testing the adequacy of a regression model, the conditional expectation of the residuals given the observed covariate is often employed to construct lack-of-fit tests. However, in the errors-in-variables model, the resiudal is biased and cannot be used directly. In this paper, by correcting for the bias, we suggest lack-of-fit tests of score type for a general linear errors-in-variables model. The polynomial model is a special case. The tests are asymptotically chi-squared under the null hypothesis. The choice of scores involved in the test statistics and the power properties are investigated. A simulation study shows that the tests perform well. Application to two data sets is also made. The approach can readily be extended to handle general parametric models.

Original languageEnglish
Pages (from-to)1049-1068
Number of pages20
JournalStatistica Sinica
Issue number4
Publication statusPublished - Oct 2005

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Bias correction
  • Errors-in-variables model
  • Lack-of-fit test


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