Asymptotic Relative Efficiency of t-test Following Transformations

  • Yeo, In-Kwon (Department of Statistics, University of Wisconsin, Madison, WI 53706, USA)
  • Published : 1997.12.01

Abstract

The two-sample t-test is not expected to be optimal when the two samples are not drawn from normal populations. According to Box and Cox (1964), the transformation is estimated to enhance the normality of the tranformed data. We investigate the asymptotic relative efficiency of the ordinary t-test versus t-test applied transformation introduced by Yeo and Johnson (1997) under Pitman local alternatives. The theoretical and simulation studies show that two-sample t-test using transformed date gives higher power than ordinary t-test for location-shift models.

References

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