The least trimmed quantile regression
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DOI: 10.1016/j.csda.2011.10.023
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Cited by:
- Mafusalov, Alexander & Uryasev, Stan, 2016. "CVaR (superquantile) norm: Stochastic case," European Journal of Operational Research, Elsevier, vol. 249(1), pages 200-208.
- N. Neykov & P. Filzmoser & P. Neytchev, 2014.
"Erratum to: Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator,"
Statistical Papers, Springer, vol. 55(3), pages 917-918, August.
- N. Neykov & P. Filzmoser & P. Neytchev, 2014. "Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator," Statistical Papers, Springer, vol. 55(1), pages 187-207, February.
- G. Zioutas & C. Chatzinakos & T. D. Nguyen & L. Pitsoulis, 2017. "Optimization techniques for multivariate least trimmed absolute deviation estimation," Journal of Combinatorial Optimization, Springer, vol. 34(3), pages 781-797, October.
- Umberto Nizza, 2023. "The expertise effect: the impact of legal specialists’ intervention on the timely delivery of laymen's judgments," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 40(2), pages 589-614, July.
- Yu-Yen Ku & Tze-Yu Yen, 2016. "Heterogeneous Effect of Financial Leverage on Corporate Performance: A Quantile Regression Analysis of Taiwanese Companies," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 1-33, September.
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