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Adaptive choice of trimming proportion in trimmed least-squares estimation

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  • Dodge, Yadolah
  • Jurecková, Jana

Abstract

We propose partially adaptive estimators of the trimming proportion [alpha] for the trimmed mean in the location modeling and for the trimmed least-squares estimator of Koenker and Bassett (1978) in the linear regression model. The adaptive estimators are based on Hájek's (1970) rank-based decision procedure which selects one of a finite family of distribution shapes and on its extension based on regression rank scores of Gutenbrunner and Jurecková (1992). The procedures are invariant to the location and scale in the location model and to the regression and scale in the regression model, respectively; hence there is no need of estimation of the pertaining parameters.

Suggested Citation

  • Dodge, Yadolah & Jurecková, Jana, 1997. "Adaptive choice of trimming proportion in trimmed least-squares estimation," Statistics & Probability Letters, Elsevier, vol. 33(2), pages 167-176, April.
    • Handle: RePEc:eee:stapro:v:33:y:1997:i:2:p:167-176
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    References listed on IDEAS

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    1. Roger Koenker & Vasco d'Orey, 1994. "A Remark on Algorithm as 229: Computing Dual Regression Quantiles and Regression Rank Scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(2), pages 410-414, June.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. Roger W. Koenker & Vasco D'Orey, 1987. "Computing Regression Quantiles," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 383-393, November.
    4. Jana Jurečková & Roger Koenker & A. Welsh, 1994. "Adaptive choice of trimming proportions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(4), pages 737-755, December.
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