Vexler Albert, Wu Chengqing, Yu Kai Fun
Department of Biostatistics, The New York State University at Buffalo, Buffalo, NY 14214, USA
Metrika. 2010 Mar 1;71(2):125-138. doi: 10.1007/s00184-008-0205-4.
We propose and examine statistical test-strategies that are somewhat between the maximum likelihood ratio and Bayes factor methods that are well addressed in the literature. The paper shows an optimality of the proposed tests of hypothesis. We demonstrate that our approach can be easily applied to practical studies, because execution of the tests does not require deriving of asymptotical analytical solutions regarding the type I error. However, when the proposed method is utilized, the classical significance level of tests can be controlled.
我们提出并检验了一些统计检验策略，这些策略介于文献中已有详细论述的最大似然比方法和贝叶斯因子方法之间。本文展示了所提出的假设检验的最优性。我们证明了我们的方法可以很容易地应用于实际研究，因为执行这些检验不需要推导关于第一类错误的渐近解析解。然而，当使用所提出的方法时，可以控制检验的经典显著性水平。
