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On the performance of the ordinary least squares method under an error component model

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Abstract

Starting from the one-dimensional results by Wang et al (1994) we consider the performance of the ordinary least squares estimator in comparison to the best linear unbiased estimator under an error component model with random effects in units and time. Upper bounds are derived for the first-order approximation to the difference between both estimators and for the spectral norm of the difference between their dispersion matrices.

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Authors and Affiliations

  1. Indian Statistical Institute, 203 B.T. Road, 700 035, Calcutta, India

    Parimal Mukhopadhyay

  2. University of Technology, Schloßgartenstraße 7, D-64289, Darmstadt, Germany

    Rainer Schwabe

Authors
  1. Parimal Mukhopadhyay
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  2. Rainer Schwabe
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Correspondence to Parimal Mukhopadhyay.

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Mukhopadhyay, P., Schwabe, R. On the performance of the ordinary least squares method under an error component model. Metrika 47, 215–226 (1998). https://doi.org/10.1007/BF02742874

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  • DOI: https://doi.org/10.1007/BF02742874

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