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Johnson's transformation two-sample trimmed t and its bootstrap method for heterogeneity and non-normality

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  • Wei-Ming Luh
  • Jiin-Huarng Guo

Abstract

The present study investigates the performance of Johnson's transformation trimmed t statistic, Welch's t test, Yuen's trimmed t , Johnson's transformation untrimmed t test, and the corresponding bootstrap methods for the two-sample case with small/unequal sample sizes when the distribution is non-normal and variances are heterogeneous. The Monte Carlo simulation is conducted in two-sided as well as one-sided tests. When the variance is proportional to the sample size, Yuen's trimmed t is as good as Johnson's transformation trimmed t . However, when the variance is disproportional to the sample size, the bootstrap Yuen's trimmed t and the bootstrap Johnson's transformation trimmed t are recommended in one-sided tests. For two-sided tests, Johnson's transformation trimmed t is not only valid but also powerful in comparison to the bootstrap methods.

Suggested Citation

  • Wei-Ming Luh & Jiin-Huarng Guo, 2000. "Johnson's transformation two-sample trimmed t and its bootstrap method for heterogeneity and non-normality," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(8), pages 965-973.
  • Handle: RePEc:taf:japsta:v:27:y:2000:i:8:p:965-973
    DOI: 10.1080/02664760050173292
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    References listed on IDEAS

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    1. Canavos, George C., 1988. "The sensitivity of the one-sample and two-sample student t statistics," Computational Statistics & Data Analysis, Elsevier, vol. 6(1), pages 39-46, January.
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