The Effects of Dependence on Nonparametric Detection

Lee D. Davisson, Edward A. Feustel, James W. Modestino

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

This paper investigates the effects of dependence on rank tests, in particular on a class of recently defined nonparametric tests called '%'mixed'%' statistical tests. It is shown that the mixed test statistic is asymptotically normal for gaussian processes with mild regularity properties justifying the use of asymptotic relative efficiency (ARE) as a figure of merit. Results are presented in terms of variations on three well-known statistics- the one- sample Wilcoxon, the two-sample Mann- Whitney, and the Kendall tau. It is found that the effects ofof dependence on ARE with respect to a parametric test can be offset to some extent by appropriately grouping sample values. If, however, a constant false-alarm rate is to be attained, either the form of the dependence must be known or some learning scheme must be applied.

Original languageEnglish (US)
Pages (from-to)32-41
Number of pages10
JournalIEEE Transactions on Information Theory
Volume16
Issue number1
DOIs
StatePublished - Jan 1970

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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