Abstract
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 language | English |
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Pages (from-to) | 1049-1068 |
Number of pages | 20 |
Journal | Statistica Sinica |
Volume | 15 |
Issue number | 4 |
Publication status | Published - 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