Statistical inference for the unbalanced two-way error component regression model with errors-in-variables

In this paper, we investigate the estimation and testing problems of unbalanced two-way error component regression model with errors-in-variables. The estimation of the unknown parameter is given based on the bias-corrected technique, and the asymptotic property of the resulting estimator is shown under some regularity conditions. For the hypothesis testing problem of restricted condition, a test statistic is constructed through the difference of the corrected residual sums of squares under the null and alternative hypotheses. Then we develop an adjusted test statistic, which follows an asymptotically standard Chi-squared distribution. Some simulation studies and a real data analysis are carried out to assess the performance of the proposed methods.