Abstract
This study examines means for inferring the distribution of the error in nonparametric regression. The central objective is to develop confidence intervals for nonparametric regression. Our computational study would seem to affirm that our methods are potentially useful in cases of small sample size or heterogeneously distributed error. Theoretical developments offer sufficient conditions for asymptotic normality.
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This work was undertaken while Dr. Rutherford was with the University of Arizona. It was supported in part by NSF grant DPP 82-19439.
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Rutherford, B., Yakowitz, S. Error inference for nonparametric regression. Ann Inst Stat Math 43, 115–129 (1991). https://doi.org/10.1007/BF00116472
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DOI: https://doi.org/10.1007/BF00116472