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An exact polynomial time algorithm for computing the least trimmed squares estimate

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  • Klouda, Karel

Abstract

An exact algorithm for computing the estimates of regression coefficients given by the least trimmed squares method is presented. The algorithm works under very weak assumptions and has polynomial complexity. Simulations show that in the case of two or three explanatory variables, the presented algorithm is often faster than the exact algorithms based on a branch-and-bound strategy whose complexity is not known. The idea behind the algorithm is based on a theoretical analysis of the respective objective function, which is also given.

Suggested Citation

  • Klouda, Karel, 2015. "An exact polynomial time algorithm for computing the least trimmed squares estimate," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 27-40.
    • Handle: RePEc:eee:csdana:v:84:y:2015:i:c:p:27-40
    DOI: 10.1016/j.csda.2014.11.001
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    References listed on IDEAS

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    1. Hawkins, Douglas M., 1994. "The feasible solution algorithm for least trimmed squares regression," Computational Statistics & Data Analysis, Elsevier, vol. 17(2), pages 185-196, February.
    2. Hawkins, Douglas M. & Olive, David, 1999. "Applications and algorithms for least trimmed sum of absolute deviations regression," Computational Statistics & Data Analysis, Elsevier, vol. 32(2), pages 119-134, December.
    3. Hofmann, Marc & Kontoghiorghes, Erricos John, 2010. "Matrix strategies for computing the least trimmed squares estimation of the general linear and SUR models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3392-3403, December.
    4. Hawkins, Douglas M. & Olive, David J., 1999. "Improved feasible solution algorithms for high breakdown estimation," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 1-11, March.
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    Cited by:

    1. Yijun Zuo, 2024. "Large Sample Behavior of the Least Trimmed Squares Estimator," Mathematics, MDPI, vol. 12(22), pages 1-21, November.

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