Abstract
Though EV model is theoretically more appropriate for applications in which measurement errors exist, people are still more inclined to use the ordinary regression models and the traditional LS method owing to the difficulties of statistical inference and computation. So it is meaningful to study the performance of LS estimate in EV model. In this article we obtain general conditions guaranteeing the asymptotic normality of the estimates of regression coefficients in the linear EV model. It is noticeable that the result is in some way different from the corresponding result in the ordinary regression model.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Drygas, H., Weak and strong consistency of the least squares estimates in regression model, Z. Wahrsch. verw. Gebiete, 34, 1976, 119–127.
Lai, T. L., Robbins, H. and Wei, C. Z., Strong consistency of least squares estimates in multiple regression, J. Multivariate Anal., 9, 1979, 343–362.
Anderson, T. W., Estimating linear statistical relationships, Ann. Statist., 12, 1984, 1–45.
Cui, H. J., Asymptotic normality of M-estimates in the EV model, Sys. Sci. and Math. Sci., 10(3), 1997, 225–236.
Fuller, W. A., Measurement Error Models, John Wiley & Sons, New York, 1987.
Liu, J. X. and Chen, X. R., Consistency of LS estimator in simple linear EV regression models, Acta Math. Sci., 25B(1), 2005, 50–58.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, J. Asymptotic Normality of LS Estimate in Simple Linear EV Regression Model. Chin. Ann. Math. Ser. B 27, 675–682 (2006). https://doi.org/10.1007/s11401-003-0409-x
Received:
Issue Date:
DOI: https://doi.org/10.1007/s11401-003-0409-x