Abstract
Arthanari and Dodge (1981) introduced an estimation method in the linear model based on a convex combination of a least squares and of a least absolute deviations estimators with a fixed weight δ, 0 ≤ δ≤ 1. They also provided two algorithms using a mathematical programming approach for finding estimates in linear regression. However, in optimizing such a convex combination, the experimenter is required to fix the value of δ or vary it at different values up to complete satisfaction in an ad hoc fashion.
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© 2000 Springer Science+Business Media New York
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Dodge, Y., Jureĉková, J. (2000). Adaptive LAD + LS Regression. In: Adaptive Regression. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8766-2_3
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DOI: https://doi.org/10.1007/978-1-4419-8766-2_3
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-6464-4
Online ISBN: 978-1-4419-8766-2
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