Facultade de Fisioterapia

Computationally Efficient Goodness-of-Fit Tests for the Error Distribution in Nonparametric Regression

Rivas-Martínez, GI; Jiménez Gamero, María Dolores
Abstract:
Several procedures have been proposed for testing goodness-of-fit to the error distribution in nonparametric regression models. The null distribution of the associated test statistics is usually approximated by means of a parametric bootstrap which, under certain conditions, provides a consistent estimator. This paper considers a goodness-of-fit test whose test statistic is an L-2 norm of the difference between the empirical characteristic function of the residuals and a parametric estimate of the characteristic function in the null hypothesis. It is proposed to approximate the null distribution through a weighted bootstrap which also produces a consistent estimator of the null distribution but, from a computational point of view, is more efficient than the parametric bootstrap.
Year:
2018
Type of Publication:
Article
Keywords:
goodness-of-fit; empirical characteristic function; regression residuals; weighted bootstrap; consistency
Journal:
Revstat-Statistical Journal
Volume:
16
Number:
1
Pages:
137-166
Month:
January
Note:
Q4 115/123 h-index 0.436 (JCR2017)
Comments:
MTM2014-55966-P and MTM2017-89422-P
Hits: 1610