BOOTSTRAP TESTS FOR THE ERROR DISTRIBUTION IN LINEAR AND NONPARAMETRIC REGRESSION MODELS
BOOTSTRAP TESTS FOR THE ERROR DISTRIBUTION IN LINEAR AND NONPARAMETRIC REGRESSION MODELS
复制标题
DOI：
10.1111/j.1467-842x.2006.00431.x
复制
发表时间：
2006-06
影响因子：
1.1
通讯作者：
N. Neumeyer;H. Dette;Eva-Renate Nagel
中科院分区：
文献类型：
--
作者：
N. Neumeyer;H. Dette;Eva-Renate Nagel
在本文中，我们研究了常见线性和非参数回归模型中误差分布参数形式假设的几种检验，这些检验基于残差的经验过程。众所周知，这种情况下的测试不是渐进无分布的，并且应用参数引导来处理这个问题。从渐进的角度并通过模拟研究来研究所得自举测试的性能。结果表明，即使对于中等大小的样本，参数引导程序也为线性和非参数回归模型中误差分布假设的拟合优度检验问题提供了可靠且易于访问的解决方案。
In this paper we investigate several tests for the hypothesis of a parametric form of the error distribution in the common linear and non‐parametric regression model, which are based on empirical processes of residuals. It is well known that tests in this context are not asymptotically distribution‐free and the parametric bootstrap is applied to deal with this problem. The performance of the resulting bootstrap test is investigated from an asymptotic point of view and by means of a simulation study. The results demonstrate that even for moderate sample sizes the parametric bootstrap provides a reliable and easy accessible solution to the problem of goodness‐of‐fit testing of assumptions regarding the error distribution in linear and non‐parametric regression models.