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Heteroscedasticity checks for regression models 总被引:1,自引:0,他引:1
For checking on heteroscedasticity in regression models, a unified approach is proposed to constructing test statistics in
parametric and nonparametric regression models. For nonparametric regression, the test is not affected sensitively by the
choice of smoothing parameters which are involved in estimation of the nonparametric regression function. The limiting null
distribution of the test statistic remains the same in a wide range of the smoothing parameters. When the covariate is one-dimensional,
the tests are, under some conditions, asymptotically distribution-free. In the high-dimensional cases, the validity of bootstrap
approximations is investigated. It is shown that a variant of the wild bootstrap is consistent while the classical bootstrap
is not in the general case, but is applicable if some extra assumption on conditional variance of the squared error is imposed.
A simulation study is performed to provide evidence of how the tests work and compare with tests that have appeared in the
literature. The approach may readily be extended to handle partial linear, and linear autoregressive models. 相似文献
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