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 language | English (US) |
|---|---|
| Pages (from-to) | 123-154 |
| Number of pages | 32 |
| Journal | Journal of Nonparametric Statistics |
| Volume | 7 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jan 1 1996 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty
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Nonparametric tests for scale and location. / Compagnone, Daniele; Denker, Manfred Heinz.
In: Journal of Nonparametric Statistics, Vol. 7, No. 2, 01.01.1996, p. 123-154.Research output: Contribution to journal › Article
TY - JOUR
T1 - Nonparametric tests for scale and location
AU - Compagnone, Daniele
AU - Denker, Manfred Heinz
PY - 1996/1/1
Y1 - 1996/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0040172691&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0040172691&partnerID=8YFLogxK
U2 - 10.1080/10485259608832695
DO - 10.1080/10485259608832695
M3 - Article
VL - 7
SP - 123
EP - 154
JO - Journal of Nonparametric Statistics
JF - Journal of Nonparametric Statistics
SN - 1048-5252
IS - 2
ER -