A goodness-of-fit test for a polynomial errors-in-variables model
Polynomial regression models with errors in variables are considered. A goodness-of-fit test is constructed, which is based on an adjusted least-squares estimator and modifies the test introduced by Zhu et al. for a linear structural model with normal distributions. In the present paper, the distributions of errors are not necessarily normal. The proposed test is based on residuals, and it is asymptotically chi-squared under null hypothesis. We discuss the power of the test and the choice of an exponent in the exponential weight function involved in test statistics.
English version (Springer): Ukrainian Mathematical Journal 56 (2004), no. 4, pp 641-661.
Citation Example: Cheng C.-L. A goodness-of-fit test for a polynomial errors-in-variables model // Ukr. Mat. Zh. - 2004. - 56, № 4. - pp. 527–543.