@article { ISI:000259127900007,
	title = {The fast-tau estimator for regression},
	journal = {JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS},
	volume = {17},
	number = {3},
	year = {2008},
	month = {SEP},
	pages = {659-682},
	publisher = {AMER STATISTICAL ASSOC},
	type = {Article},
	address = {732 N WASHINGTON ST, ALEXANDRIA, VA 22314-1943 USA},
	abstract = {Yohai and Zamar{\textquoteright}s tau-estimators of regression have excellent statistical properties but are nevertheless rarely used in practice because of a lack of available software and the general impression that tau-estimators are difficult to approximate. We will show, however, that the computational difficulties of approximating tau-estimators are similar in nature to those of the more popular S-estimators. The main goal of this article is to compare an approximating algorithm for tau-estimators based on random resampling with some alternative heuristic search algorithms. We show that the former is not only simpler, but that when enhanced by local improvement steps it generally outperforms the considered heuristic search algorithms, even when these heuristic algorithms also incorporate local improvement steps. Additionally, we show that the random resampling algorithm for approximating tau-estimators has favorable statistical properties compared to the analogous and widely used algorithms for S- and least trimmed squares estimators.},
	keywords = {random resampling, Robust regression, simulated annealing, tabu search},
	issn = {1061-8600},
	doi = {10.1198/106186008X343785},
	author = {Salibian-Barrera, Mat{\'\i}as and Willems, Gert and Zamar, Ruben}
}
@article { ISI:000259127900007,
	title = {The fast-tau estimator for regression},
	journal = {JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS},
	volume = {17},
	number = {3},
	year = {2008},
	month = {SEP},
	pages = {659-682},
	publisher = {AMER STATISTICAL ASSOC},
	type = {Article},
	address = {732 N WASHINGTON ST, ALEXANDRIA, VA 22314-1943 USA},
	abstract = {<p>Yohai and Zamar{\textquoteright}s tau-estimators of regression have excellent statistical properties but are nevertheless rarely used in practice because of a lack of available software and the general impression that tau-estimators are difficult to approximate. We will show, however, that the computational difficulties of approximating tau-estimators are similar in nature to those of the more popular S-estimators. The main goal of this article is to compare an approximating algorithm for tau-estimators based on random resampling with some alternative heuristic search algorithms. We show that the former is not only simpler, but that when enhanced by local improvement steps it generally outperforms the considered heuristic search algorithms, even when these heuristic algorithms also incorporate local improvement steps. Additionally, we show that the random resampling algorithm for approximating tau-estimators has favorable statistical properties compared to the analogous and widely used algorithms for S- and least trimmed squares estimators.</p>
},
	keywords = {random resampling, Robust regression, simulated annealing, tabu search},
	issn = {1061-8600},
	doi = {10.1198/106186008X343785},
	author = {Salibian-Barrera, Mat{\'\i}as and Willems, Gert and Zamar, Ruben}
}
