The Annals of Mathematical Statistics

Domains of Optimality of Tests in Simple Random Sampling

David K. Hildebrand

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This paper deals with the structure of sets $\Omega$ of distributions for which a particular test is the most powerful for testing a simple hypothesis $H:f = f_0 \operatorname{vs.} K:f \varepsilon\Omega$, that is, with the domain of optimality of a test. The context is restricted to these $\Omega$ consisting of probabilities having continuous positive densities, and to one-sample tests. The important concept is that of a family of tests, one for each significance level. This concept allows us to use the full power of the Neyman-Pearson Lemma. The main results are: (1) The domain of optimality of a test family $\Phi$ is essentially a multiplicatively-convex (convex in the logarithms) cone; hence there are distributions both "near to" and "far from" the null distribution for which $\Phi$ is optimal. (Theorems 1, 2, and 3). (2) If $\Phi$ is uniformly most powerful for testing $H:f = f_0 \operatorname{vs.} K:f \varepsilon\Omega$ with $n \geqq 2$ then the class of distributions has a monotone likelihood ratio. (Theorem 4).

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Ann. Math. Statist., Volume 40, Number 1 (1969), 308-312.

First available in Project Euclid: 27 April 2007

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Hildebrand, David K. Domains of Optimality of Tests in Simple Random Sampling. Ann. Math. Statist. 40 (1969), no. 1, 308--312. doi:10.1214/aoms/1177697827.

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