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Measurement Error Models with Nonconstant Covariance Matrices

Author

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  • Arellano-Valle, Reinaldo B.
  • Bolfarine, Heleno
  • Gasco, Loreta

Abstract

In this paper we consider measurement error models when the observed random vectors are independent and have mean vector and covariance matrix changing with each observation. The asymptotic behavior of the sample mean vector and the sample covariance matrix are studied for such models. Using the derived results, we study the case of the elliptical multiplicative error-in-variables models, providing formal justification for the asymptotic distribution of consistent slope parameter estimators. The model considered extends a normal model previously considered in the literature. Asymptotic relative efficiencies comparing several estimators are also reported.

Suggested Citation

  • Arellano-Valle, Reinaldo B. & Bolfarine, Heleno & Gasco, Loreta, 2002. "Measurement Error Models with Nonconstant Covariance Matrices," Journal of Multivariate Analysis, Elsevier, vol. 82(2), pages 395-415, August.
    • Handle: RePEc:eee:jmvana:v:82:y:2002:i:2:p:395-415
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    References listed on IDEAS

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    1. Chan, Lai K. & Mak, Tak K., 1979. "On the Maximum Likelihood estimation of a linear structural relationship when the intercept is known," Journal of Multivariate Analysis, Elsevier, vol. 9(2), pages 304-313, June.
    2. Vilca-Labra, F. & Arellano-Valle, R. B. & Bolfarine, H., 1998. "Elliptical Functional Models," Journal of Multivariate Analysis, Elsevier, vol. 65(1), pages 36-57, April.
    3. Stephen J. Iturria & Raymond J. Carroll & David Firth, 1999. "Polynomial Regression and Estimating Functions in the Presence of Multiplicative Measurement Error," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 547-561.
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    Cited by:

    1. Arellano-Valle, R.B. & Ozan, S. & Bolfarine, H. & Lachos, V.H., 2005. "Skew normal measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 96(2), pages 265-281, October.

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