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B1294
Title: Faster computationally approximation for comparing the error distributions in nonparametric regression Authors:  Gustavo Rivas - National University of Asuncion (Paraguay) [presenting]
Maria Dolores Jimenez-Gamero - Universidad de Sevilla (Spain)
Abstract: Several procedures have been proposed for testing the equality of error distributions in two or more nonparametric regression models. We deal with methods based on comparing estimators of the cumulative distribution function (CDF) of the errors in each population to an estimator of the common CDF under the null hypothesis. The null distribution of the associated test statistics has been approximated by means of a smooth bootstrap (SB) estimator. The proposal is to approximate their null distribution through a weighted bootstrap. It is shown that it produces a consistent estimator. The finite sample performance of this approximation is assessed by means of a simulation study, where it is also compared to the SB. From a computational point of view, the proposed approximation is more efficient than the one provided by the SB.