Approximate transformation trimmed mean methods to the test of simple linear regression slope equality

Wei-Ming Luh and Jiin-Huarng Guo

Journal of Applied Statistics, 2000, vol. 27, issue 7, 843-857

Abstract: To deal with the problem of non-normality and heteroscedasticity, the current study proposes applying approximate transformation trimmed mean methods to the test of simple linear regression slope equality. The distribution-free slope estimates are first trimmed on both sides and then the test statistic t is transformed by Johnson's method for each group to correct non-normality. Lastly, an approximate test such as the James second-order test, the Welch test, or the DeShon-Alexander test, which are robust for heterogeneous variances, is applied to test the equality of regression slopes. Bootstrap methods and Monte Carlo simulation results show that the proposed methods provide protection against both unusual y values, as well as unusual x values. The new methods are valid alternatives for testing the simple linear regression slopes when heteroscedastic variances and nonnormality are present.

Date: 2000
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DOI: 10.1080/02664760050120533

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