Bootstrap approximations in model checks for regression
Bootstrap approximations in model checks for regression
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DOI：
10.2307/2669611
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发表时间：
1998-03-01
影响因子：
3.7
通讯作者：
Quindimil, MP
中科院分区：
文献类型：
--
作者：
Stute, W;Manteiga, WG;Quindimil, MP
设M = {m（theta）：theta是Theta的一个元素}是未知回归函数m的参数模型。例如，M可以由具有给定次数界限的所有多项式或三角多项式组成。为了检查完整模型M（即，为了检验H-0：m是M的一个元素），众所周知，最佳检验应该基于由残差标记的回归量的经验过程。在这篇文章中，我们表明，这个过程的分布可以近似的野生自助。该方法适用于模拟数据集以及真实的数据。
Let M = {m(theta): theta is an element of Theta} be a parametric model for an unknown regression function m. For example, M may consist of all polynomials or trigonometric polynomials with a given bound on the degree. To check the full model M (i.e., to test for H-0:m is an element of M), it is known that optimal tests should be based on the empirical process of the regressors marked by the residuals. In this article we show that the distribution of this process may be approximated by the wild bootstrap. The method is applied to simulated datasets as well as to real data.