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Combining Likelihood Information from Independent Investigations

机译:合并来自独立调查的可能性信息

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Fisher [1] proposed a simple method to combine p-values from independent investigations without using detailed information of the original data. In recent years, likelihood-based asymptotic methods have been developed to produce highly accurate p-values. These likelihood-based methods generally required the likelihood function and the standardized maximum likelihood estimates departure calculated in the canonical parameter scale. In this paper, a method is proposed to obtain a p-value by combining the likelihood functions and the standardized maximum likelihood estimates departure of independent investigations for testing a scalar parameter of interest. Examples are presented to illustrate the application of the proposed method and simulation studies are performed to compare the accuracy of the proposed method with Fisher’s method.
机译:Fisher [1]提出了一种简单的方法来合并独立调查的p值,而无需使用原始数据的详细信息。近年来,已开发出基于似然的渐近方法以产生高度准确的p值。这些基于似然的方法通常需要按照规范参数尺度计算出的似然函数和标准化的最大似然估计偏差。在本文中,提出了一种方法,该方法通过将似然函数和标准化的最大似然估计值相结合来获得独立p值,以测试感兴趣的标量参数,从而获得p值。举例说明了该方法的应用,并进行了仿真研究,以比较该方法与Fisher方法的准确性。

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