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逆高斯分布函数的估计AI 解读

Minimum variance unbiased estimates of the inverse Gaussian distribution function for all possible cases are given. A direct relationship is established between its density function and the normal density function, which throws more light on its salient features and possibly on its application in statistical inference. It is shown that the estimates are very similar in nature to those of the normal distribution and can be evaluated from the normal and Student's t…

Fisher独立检验合并方法的渐近最优性（二）AI 解读

It was shown by Littell and Folks [3] that Fisher's method of combining independent tests is asymptotically optimal among four commonly used methods of combining, according to Bahadur relative efficiency. The result of the present article is that Fisher's method is asymptotically optimal among essentially all methods of combining independent tests.

Fisher 独立检验合并方法的渐近最优性AI 解读

Four methods of combining independent tests of hypothesis are compared via exact Bahadur relative efficiency. The methods considered are Fisher's method, the mean of the normal transforms of the significance levels, the maximum significance level, and the minimum significance level. None of these is uniformly more powerful than the others, but, according to Bahadur efficiency, Fisher's method is the most efficient of the four. In some cases, Fisher's method is most efficient…

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