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Consistent estimator in multivariate errors-in-variables model in the case of unknown error covariance structure

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Ukrainian Mathematical Journal Aims and scope

Abstract

We consider a linear multivariate errors-in-variables model AX ≈ B, where the matrices A and B are observed with errors and the matrix parameter X is to be estimated. In the case of lack of information about the error covariance structure, we propose an estimator that converges in probability to X as the number of rows in A tends to infinity. Sufficient conditions for this convergence and for the asymptotic normality of the estimator are found.

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Authors and Affiliations

  1. Shevchenko Kyiv National University, Kyiv

    O. H. Kukush & M. Ya. Polekha

Authors
  1. O. H. Kukush
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  2. M. Ya. Polekha
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Translated from Ukrains’kyi Matematychnyi Zhurnal, Vol. 59, No. 8, pp. 1026–1033, August, 2007.

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Kukush, O.H., Polekha, M.Y. Consistent estimator in multivariate errors-in-variables model in the case of unknown error covariance structure. Ukr Math J 59, 1137–1147 (2007). https://doi.org/10.1007/s11253-007-0075-3

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  • DOI: https://doi.org/10.1007/s11253-007-0075-3

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