Reweighted Anderson-Darling Tests of Goodness-of-Fit
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Assessing goodness-of-fit is challenging because theoretically there is no uniformly powerful test, whereas in practice the question `what would be a preferable default test?' is important to applied statisticians. To take a look at this so-called omnibus testing problem, this paper considers the class of reweighted Anderson-Darling tests and makes two fold contributions. The first contribution is to provide a geometric understanding of the problem via establishing an explicit one-to-one correspondence between the weights and their focal directions of deviations of the distributions under alternative hypothesis from those under the null. It is argued that the weights that produce the test statistic with minimum variance can serve as a general-purpose test. In addition, this default or optimal weights-based test is found to be practically equivalent to the Zhang test, which has been commonly perceived powerful. The second contribution is to establish new large-sample results. It is shown that like Anderson-Darling, the minimum variance test statistic under the null has the same distribution as that of a weighted sum of an infinite number of independent squared normal random variables. These theoretical results are shown to be useful for large sample-based approximations. Finally, the paper concludes with a few remarks, including how the present approach can be extended to create new multinomial goodness-of-fit tests.
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