Testing independence in nonparametric regression
We propose a new test for independence of error and covariate in a nonparametric regression model. The test statistic is based on a kernel estimator for the L2-distance between the conditional distribution and the unconditional distribution of the covariates. In contrast to tests so far available in literature, the test can be applied in the important case of multivariate covariates. It can also be adjusted for models with heteroscedastic variance. Asymptotic normality of the test statistic is shown. Simulation results and a real data example are presented.
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Volume (Year): 100 (2009)
Issue (Month): 7 (August)
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References listed on IDEAS
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- Einmahl, John H.J. & Van Keilegom, Ingrid, 2008.
"Specification tests in nonparametric regression,"
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- Einmahl, J.H.J. & van Keilegom, I., 2008. "Tests for independence in nonparametric regression," Other publications TiSEM 4356c520-d1d5-4156-b5b7-0, Tilburg University, School of Economics and Management.
- Ingrid Keilegom & Wenceslao González Manteiga & César Sánchez Sellero, 2008. "Goodness-of-fit tests in parametric regression based on the estimation of the error distribution," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(2), pages 401-415, August.
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