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On the efficient computation of robust regression estimators

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  • Flores, Salvador

Abstract

The problem of providing efficient and reliable robust regression algorithms is considered. The impact of global optimization methods, such as stopping conditions and clustering techniques, in the calculation of robust regression estimators is investigated. The use of stopping conditions permits us to devise new algorithms that perform as well as the existing algorithms in less time and with adaptive algorithm parameters. Clustering global optimization is shown to be a general framework encompassing many of the existing algorithms.

Suggested Citation

  • Flores, Salvador, 2010. "On the efficient computation of robust regression estimators," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3044-3056, December.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:12:p:3044-3056
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    References listed on IDEAS

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    1. Agullo, Jose, 2001. "New algorithms for computing the least trimmed squares regression estimator," Computational Statistics & Data Analysis, Elsevier, vol. 36(4), pages 425-439, June.
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    Cited by:

    1. Torti, Francesca & Perrotta, Domenico & Atkinson, Anthony C. & Riani, Marco, 2012. "Benchmark testing of algorithms for very robust regression: FS, LMS and LTS," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2501-2512.

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