RT info:eu-repo/semantics/article
T1 Computationally efficient approximations for independence tests in non-parametric regression
A1 Rivas Martínez, Gustavo Ignacio
A1 Jiménez Gamero, María Dolores
AB A common assumption in non-parametric regression models is the independence of the covariate and the error. Some procedures have been suggested for testing that hypothesis. This paper considers a test, whose test statistic compares estimators of the joint and the product of the marginal characteristic functions of the covariate and the error. It is proposed to approximate the null distribution of such statistic by means of a weighted bootstrap estimator. The resulting test is able to detect any fixed alternative as well as local alternatives converging to the null at the rate n−1/2𝑛−1/2, n denoting the sample size. The finite sample performance of this approximation is assessed by means of a simulation study, where it is also compared with other estimators. This study reveals that, from a computational point of view, the proposed approximation is very efficient. Two real data set applications are also included.
PB Taylor & Francis
YR 2020
FD 2020-11-12
LK http://hdl.handle.net/20.500.14066/4432
UL http://hdl.handle.net/20.500.14066/4432
LA eng
NO Rivas Martínez, G. I., & Jiménez Gamero, M. D. (2020). Computationally efficient approximations for independence tests in non-parametric regression. Journal of Statistical Computation and Simulation, 91(6), 1134-1154. https://doi.org/10.1080/00949655.2020.1843038
NO Correspondence: gusyri@hotmail.com
NO Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 27-nov-2024