论文标题
Bartlett和Bartlett-type校正在异性对称非线性回归模型中
Bartlett and Bartlett-type corrections in heteroscedastic symmetric nonlinear regression models
论文作者
论文摘要
本文为Bartlett和Bartlett型校正因子提供了一般表达，可用于测试异源性对称非线性模型中的分散参数。这类回归模型可能对于模拟包含外围观察结果的数据可能有用。我们考虑有关分散参数向量的分区，以测试感兴趣的参数。此外，我们开发了蒙特卡洛模拟，以比较提出的校正测试的有限样本性能与通常的和修改的得分测试，可能性和梯度测试，Bartlett-type校正得分测试以及Bootstrap校正的测试。我们的仿真结果有利于分数和梯度校正测试以及自举测试。出于说明目的提出了经验应用。
This paper provides general expression for Bartlett and Bartlett-type correction factors for the likelihood ratio and gradient statistics to test the dispersion parameter in heteroscedastic symmetric nonlinear models. This class of regression models is potentially useful for modeling data containing outlying observations. We consider a partition on the dispersion parameter vector in order to test the parameters of interest. Furthermore, we develop Monte Carlo simulations to compare the finite sample performances of the corrected tests proposed with the usual and modified score tests, likelihood and gradient tests, the Bartlett-type corrected score test and bootstrap corrected tests. Our simulation results favor the score and gradient corrected tests as well as the bootstrap tests. An empirical application is presented for illustrative purposes.
