MODEL CHECKING FOR GENERAL LINEAR ERROR-IN-COVARIABLES MODEL WITH VALIDATION DATA

In this paper, model checking problem is considered for general linear model when covariables are measured with error and an independent validation data set is available. Without assuming any error model structure between the true variable and the surrogate variable, the author first apply nonparametric method to model the relationship between the true variable and the surrogate variable with the help of the validation sample. Then the author construct a score-type test statistic through model adjustment. The large sample behaviors of the score-type test statistic are investigated. It is shown that the test is consistent and can detect the alternative hypothesis close to the null hypothesis at the rate n(-r) with 0 <= r <= 1/2. Simulation results indicate that the proposed method works well.