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Asymptotic normality of total least squares estimator in a multivariate errors-in-variables model $AX=B$

Research paper by Alexander Kukush, Yaroslav Tsaregorodtsev

Indexed on: 13 Jul '16Published on: 13 Jul '16Published in: Mathematics - Probability



Abstract

We consider a multivariate functional measurement error model $AX\approx B$. The errors in $[A,B]$ are uncorrelated, row-wise independent, and have equal (unknown) variances. We study the total least squares estimator of $X$, which, in the case of normal errors, coincides with the maximum likelihood one. We give conditions for asymptotic normality of the estimator when the number of rows in $A$ is increasing. Under mild assumptions, the covariance structure of the limit Gaussian random matrix is nonsingular. For normal errors, the results can be used to construct an asymptotic confidence interval for a linear functional of $X$.