Restricted Regression Quantiles
Regression quantiles can be used as prediction intervals for the response variable. But such applications are often hampered by the problem of quantile crossing in finite sample cases. This article examines the efficiency properties of restricted regression quantiles that are proposed by X. He (1997, Amer. Statist.51, 186-192) to overcome the crossing problem of the usual regression quantiles of R. Koenker and G. Bassett (1978, Econometrica46, 33-50) for linear models. An example using esterase assay data is presented to illustrate the use of restricted regression quantiles in constructing calibration intervals.
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Volume (Year): 72 (2000)
Issue (Month): 1 (January)
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Koenker, Roger & Zhao, Quanshui, 1996. "Conditional Quantile Estimation and Inference for Arch Models," Econometric Theory, Cambridge University Press, vol. 12(05), pages 793-813, December.
- Koenker, Roger, 1984. "A note on L-estimates for linear models," Statistics & Probability Letters, Elsevier, vol. 2(6), pages 323-325, December.
- Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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