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A Central Limit Theorem under Contiguous Alternatives
K. Behnen and G. Neuhaus
The Annals of Statistics
Vol. 3, No. 6 (Nov., 1975), pp. 1349-1353
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2958253
Page Count: 5
You can always find the topics here!Topics: Contiguity, Random variables, Truncation, Rank tests, Statism, Central limit theorem, Necessary conditions, Perceptron convergence procedure, Gaussian distributions
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In the statistical literature Le Cam's third lemma (cf. Hájek and Šidák (1967), page 208) is extensively used in order to get asymptotic normality of a statistic Sn under contiguous alternatives from asymptotic normality of Sn under the nullhypothesis. Since Le Cam's lemma utilizes the joint asymptotic normality of Sn and log-likelihood-ratio log Ln, which is a sufficient but in general not a necessary condition for contiguity, it is not possible to get asymptotic normality of Sn for all contiguous alternatives from this lemma. On the other hand one is interested in the limiting distribution of Sn under all contiguous alternatives in order to get general power and efficiency results for the respective tests. In this paper we utilize a truncation method in order to prove asymptotic normality under all contiguous alternatives from asymptotic normality under the nullhypothesis for sums of independent random variables which are interesting in rank test theory, since they often are asymptotically equivalent to certain rank statistics under the nullhypothesis, and thus under contiguous alternatives, too.
The Annals of Statistics © 1975 Institute of Mathematical Statistics