The Annals of Statistics

Estimation for the Multivariate Errors-in-Variables Model with Estimated Error Covariance Matrix

Yasuo Amemiya and Wayne A. Fuller

Full-text: Open access

Abstract

The errors-in-variables model in which the unobserved true values satisfy multiple linear restrictions is considered. Under the assumptions that the unobservable true values are normally distributed and that an estimator of the covariance matrix of the measurement error is available, the maximum likelihood estimators are derived. The limiting properties of the estimators are obtained for a wide range of assumptions, including the assumption of fixed true values.

Article information

Source
Ann. Statist., Volume 12, Number 2 (1984), 497-509.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176346502

Digital Object Identifier
doi:10.1214/aos/1176346502

Mathematical Reviews number (MathSciNet)
MR740908

Zentralblatt MATH identifier
0543.62042

JSTOR
links.jstor.org

Subjects
Primary: 62J99: None of the above, but in this section
Secondary: 62H12: Estimation 62H25: Factor analysis and principal components; correspondence analysis 62F10: Point estimation 62F12: Asymptotic properties of estimators

Keywords
Errors-in-variables structural relationship functional relationship multivariate regression model maximum likelihood estimator asymptotic distribution measurement errors

Citation

Amemiya, Yasuo; Fuller, Wayne A. Estimation for the Multivariate Errors-in-Variables Model with Estimated Error Covariance Matrix. Ann. Statist. 12 (1984), no. 2, 497--509. doi:10.1214/aos/1176346502. https://projecteuclid.org/euclid.aos/1176346502


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