The score test statistic using the observed information is easy to compute numerically. Its large sample distribution under the null hypothesis is well known and is equivalent to that of the score test based on the expected information, the likelihood-ratio test and the Wald test. However, several authors have noted that under the alternative this no longer holds and in particular the statistic can take negative values. Here we examine the score test using the observed information in the context of comparing two binomial proportions under imperfect detection, a common problem in ecology when studying occurrence of species. We demonstrate through a combination of simulations and analysis that a modified rule that rejects the null hypothesis when the observed score statistic is larger than the usual chi-square cut-off or is negative has power that is mostly greater to any other test. In addition consistency is largely restored. Our new test is easy to use and inference is always possible.
This talk is based on a joint work with G. Guillera-Arroita, R.M. Huggins and B.J.T. Morgan.
Host University: La Trobe University
Seminar Convener: Dr N. Karavarsamis (La Trobe University)
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