Rank-based testing in linear models with stable errors
Rank-based testing in linear models with stable errors
复制标题
具有稳定误差的线性模型中基于等级的测试
DOI：
10.1080/10485252.2010.525234
复制
发表时间：
2011
影响因子：
1.2
通讯作者：
David Veredas
中科院分区：
文献类型：
--
作者：
M. Hallin;Yvik Swan;Thomas Verdebout;David Veredas
考虑了具有稳定误差密度的线性模型，建立了它们关于回归参数的局部渐近正态性。我们使用这个结果，结合Le Cam的第三个引理，在α-稳定密度下，在不同的偏度参数和尾部指数值下，获得了各种经典秩检验（Wilcoxon，货车der Waerden和中位数检验的回归和方差分析）的局部幂和渐近相对效率。相同的结果被用来构建新的秩检验，基于“稳定分数”，在指定的稳定密度实现参数最优。蒙特卡洛研究进行了有限样本的相对性能进行比较。
Linear models with stable error densities are considered, and their local asymptotic normality with respect to the regression parameter is established. We use this result, combined with Le Cam's third lemma, to obtain local powers and asymptotic relative efficiencies for various classical rank tests (the regression and analysis of variance counterparts of the Wilcoxon, van der Waerden and median tests) under α-stable densities with various values of the skewness parameter and tail index. The same results are used to construct new rank tests, based on ‘stable scores’, achieving parametric optimality at specified stable densities. A Monte Carlo study is conducted to compare their finite-sample relative performances.