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Least Trimmed Squares Estimator in the Errors-in-Variables Model

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  • Kang-Mo Jung

Abstract

We propose a robust estimator in the errors-in-variables model using the least trimmed squares estimator. We call this estimator the orthogonal least trimmed squares (OLTS) estimator. We show that the OLTS estimator has the high breakdown point and appropriate equivariance properties. We develop an algorithm for the OLTS estimate. Simulations are performed to compare the efficiencies of the OLTS estimates with the total least squares (TLS) estimates and a numerical example is given to illustrate the effectiveness of the estimate.

Suggested Citation

  • Kang-Mo Jung, 2007. "Least Trimmed Squares Estimator in the Errors-in-Variables Model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(3), pages 331-338.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:3:p:331-338
    DOI: 10.1080/02664760601004973
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    References listed on IDEAS

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    1. Fekri, M. & Ruiz-Gazen, A., 2004. "Robust weighted orthogonal regression in the errors-in-variables model," Journal of Multivariate Analysis, Elsevier, vol. 88(1), pages 89-108, January.
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    Cited by:

    1. Eric Blankmeyer, 2018. "Measurement Errors as Bad Leverage Points," Papers 1807.02814, arXiv.org, revised Mar 2020.

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