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Robust prediction limits based on M-estimators

Author

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  • Giummolè, F.
  • Ventura, L.

Abstract

We discuss a robust solution to the problem of prediction. Extending Barndorff-Nielsen and Cox [1996. Prediction and asymptotics. Bernoulli 2, 319-340] and Vidoni [1998. A note on modified estimative prediction limits and distributions. Biometrika 85, 949-953], we propose improved prediction limits based on M-estimators. To compute them, the expressions of the bias and variance of an M-estimator are required. In view of this, a general asymptotic approximation for the bias of an M-estimator is derived. Moreover, by means of comparative studies in the context of affine transformation models, we show that the proposed robust procedure for prediction can be successfully used in a parametric setting.

Suggested Citation

  • Giummolè, F. & Ventura, L., 2006. "Robust prediction limits based on M-estimators," Statistics & Probability Letters, Elsevier, vol. 76(16), pages 1735-1740, October.
    • Handle: RePEc:eee:stapro:v:76:y:2006:i:16:p:1735-1740
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    References listed on IDEAS

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    1. Fisher, Amy & Horn, Paul S., 1994. "Robust prediction intervals in a regression setting," Computational Statistics & Data Analysis, Elsevier, vol. 17(2), pages 129-140, February.
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