Restricted estimation and testing of hypothesis in linear measurement errors models

ABSTRACT

In this article, the linear models with measurement error both in the response and in the covariates are considered. Following Shalabh et al. (2007 Shalabh, Garg, G., Misra, N. (2007). Restricted regression estimation in measurement error models. Comput. Stat. Data Anal. 52:1149–1166.Crossref], Web of Science ®] , Google Scholar], 2009 Shalabh, Garg, G., Misra, N. (2009). Use of prior information in the consistent estimation of regression coefficients in measurement error models. J. Multivariate Anal. 100:1498–1520.Crossref], Web of Science ®] , Google Scholar]), we propose several restricted estimators for the regression coefficients. The consistency and asymptotic normality of the restricted estimators are established. Furthermore, we also discuss the superiority of the restricted estimators to unrestricted estimators under Pitman closeness criterion. We also develop several variance estimators and establish their asymptotic distributions. Wald-type statistics are constructed for testing the linear restrictions. Finally, Monte Carlo simulations are conducted to illustrate the finite-sample properties of the proposed estimators.