Abstract
The asymptotic normality of robust estimators suggests that shrinking techniques previously considered for least squares regression are appropriate in robust regression as well. Moreover, the noisy nature of the data frequently encountered in robust regression problems makes the use of shrinking estimators particularly advantageous. Asymptotic and finite sample results and a short simulation demonstrate that shrinking techniques can indeed improve a robust estimator’s performance.
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© 1989 Springer-Verlag New York, Inc.
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Schmoyer, R.L., Arnold, S.F. (1989). Shrinking Techniques for Robust Regression. In: Gleser, L.J., Perlman, M.D., Press, S.J., Sampson, A.R. (eds) Contributions to Probability and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3678-8_26
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DOI: https://doi.org/10.1007/978-1-4612-3678-8_26
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