A Parametric Bootstrap Approach for a One-Way Error Component Regression Model with Measurement Errors

In this paper, a one-way error component regression model with measurement errors is considered. The unknown parameter vector is estimated by using the bias-corrected method, and its corresponding asymptotic properties are also developed. For the hypothesis testing problem of the vector of the coefficient parameter in the model, a parametric bootstrap (PB) method is proposed. Under various sample sizes and parameter configurations, the effectiveness of our proposed PB test method is discussed by using some numerical simulations and a real data analysis.

Automated summary

This paper considers a one-way error component regression model with measurement errors. The unknown parameter vector is estimated using the bias-corrected method, and its asymptotic properties are developed. For hypothesis testing of the coefficient parameter vector, a parametric bootstrap (PB) method is proposed. The effectiveness of the proposed PB test method is discussed through numerical simulations and real data analysis under different sample sizes and parameter configurations.