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Computationally efficient goodness-of-fit tests for the error distribution in nonparametric regression

Opened Access Computationally efficient goodness-of-fit tests for the error distribution in nonparametric regression
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Autor: Rivas Martínez, Gustavo Ignacio
Jiménez Gamero, María Dolores
Departamento: Universidad de Sevilla. Departamento de Estadística e Investigación Operativa
Fecha: 2018-01
Publicado en: Revstat Statistical Journal, 16 (1), 137-166.
Tipo de documento: Artículo
Resumen: 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 L2 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.
Tamaño: 279.5Kb
Formato: PDF

URI: https://hdl.handle.net/11441/70740

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