Some pathological regression asymptotics under stable conditions
We consider a simple through-the-origin linear regression example introduced by Rousseeuw, van Aelst and Hubert (J. Amer. Stat. Assoc., 94 (1994) 419-434). It is shown that the conventional least squares and least absolute error estimators converge in distribution without normalization and consequently are inconsistent. A class of weighted median regression estimators, including the maximum depth estimator of Rousseeuw and Hubert (J. Amer. Stat. Assoc., 94 (1999) 388-402), are shown to converge at rate n-1. Finally, the maximum likelihood estimator is considered, and we observe that there exist estimators that converge at rate n-2. The results illustrate some interesting, albeit somewhat pathological, aspects of stable-law convergence.
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Volume (Year): 50 (2000)
Issue (Month): 3 (November)
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References listed on IDEAS
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- Kooperberg, Charles & Stone, Charles J., 1991. "A study of logspline density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 12(3), pages 327-347, November.
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- Weron, Rafal, 1996. "On the Chambers-Mallows-Stuck method for simulating skewed stable random variables," Statistics & Probability Letters, Elsevier, vol. 28(2), pages 165-171, June.
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