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| Content Provider | Springer Nature Link |
|---|---|
| Author | Riani, Marco |
| Copyright Year | 2004 |
| Abstract | The assumption of multivariate normality provides the customary powerful and convenient ways of analysing multivariate data: if the data are not normal, the analysis may often be simplified by an appropriate transformation. In this context, the most widely used test is the likelihood ratio, which requires the maximum likelihood estimate of the transformation parameter for each variable. Given that this estimate can only be found numerically, when the number of variables is large (> 20) it is impossible or infeasible to compute the test. In this paper we introduce alternative tests which do not require the maximum likelihood estimate of the transformation parameters and prove algebraically their relationships. We also give insights both using theoretical arguments and a robust simulation study, based on the forward search algorithm, about the distribution of the tests previously introduced. |
| Starting Page | 179 |
| Ending Page | 196 |
| Page Count | 18 |
| File Format | |
| ISSN | 16182510 |
| Journal | Statistical Methods and Applications |
| Volume Number | 13 |
| Issue Number | 2 |
| e-ISSN | 1613981X |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2004-01-01 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Box-Cox transformation Forward search Score statistic Wald statistic Transformation to normality Robust methods |
| Content Type | Text |
| Resource Type | Article |
| Subject | Statistics and Probability Statistics, Probability and Uncertainty |
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