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Robust regression for estimating the Burr XII parameters with outliers

Author

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  • Fu-Kwun Wang
  • Yung-Fu Cheng

Abstract

The Burr XII distribution offers a more flexible alternative to the lognormal, log-logistic and Weibull distributions. Outliers can occur during reliability life testing. Thus, we need an efficient method to estimate the parameters of the Burr XII distribution for censored data with outliers. The objective of this paper is to present a robust regression (RR) method called M-estimator to estimate the parameters of a two-parameter Burr XII distribution based on the probability plotting procedure for both the complete and multiply-censored data with outliers. The simulation results show that the RR method outperforms the unweighted least squares and maximum likelihood methods in most cases in terms of bias and errors in the root mean square.

Suggested Citation

  • Fu-Kwun Wang & Yung-Fu Cheng, 2010. "Robust regression for estimating the Burr XII parameters with outliers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 807-819.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:807-819
    DOI: 10.1080/02664760902906231
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    Cited by:

    1. Ilhan Usta, 2013. "Different estimation methods for the parameters of the extended Burr XII distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 397-414, February.

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