Nonparametric tests for scale and location

Daniele Compagnone, Manfred Heinz Denker

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

We prove asymptotic normality of simple linear rank statistics when the observations have U-statistic structure. This type of statistic can be used for testing the location parameter and/or the scale parameter in a scale-location model. It is shown that the tests for the location parameter are asymptotically efficient under normality using a normal score function and are also as efficient as the t-test when the dimension of the kernel function tends to infinity and when Wilcoxon scores are used. For scaling parameters we propose a generalization of tests by Lehmann and Moses for scale effects. We describe a simulation study which shows that these new tests perform better than the ones previously used.

Original languageEnglish (US)
Pages (from-to)123-154
Number of pages32
JournalJournal of Nonparametric Statistics
Volume7
Issue number2
DOIs
StatePublished - Jan 1 1996

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Nonparametric tests for scale and location'. Together they form a unique fingerprint.

Cite this