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Robust linear regression in replicated measurement error models

Author

Listed:
  • Carroll, R. J.
  • Eltinge, J. L.
  • Ruppert, D.

Abstract

We propose robust and bounded influence methods for linear regression when some of the predictors are measured with error. We address the important special case that the surrogate predictors are replicated, and that the measurement errors in response and predictors are correlated. The robust methods proposed are variants of the so-called Mallows class of estimates. The resulting estimators are easily computed and have a simple asymptotic theory. An example is used to illustrate the results.

Suggested Citation

  • Carroll, R. J. & Eltinge, J. L. & Ruppert, D., 1993. "Robust linear regression in replicated measurement error models," Statistics & Probability Letters, Elsevier, vol. 16(3), pages 169-175, February.
    • Handle: RePEc:eee:stapro:v:16:y:1993:i:3:p:169-175
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    Cited by:

    1. Delaigle, Aurore & Meister, Alexander, 2007. "Nonparametric Regression Estimation in the Heteroscedastic Errors-in-Variables Problem," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1416-1426, December.
    2. A. Basu & S. Sarkar, 1997. "Robust estimation in the errors variables model via weighted likelihood estimating equations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 6(1), pages 187-203, June.

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