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

Wei-Ming Luh, Jiin Huarng Guo

Research output: Contribution to journalArticle

6 Citations (Scopus)

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 non-normality are present.

Original languageEnglish
Pages (from-to)843-857
Number of pages15
JournalJournal of Applied Statistics
Volume27
Issue number7
DOIs
Publication statusPublished - 2000 Jan 1

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Trimmed Mean
Simple Linear Regression
Slope
Equality
Non-normality
Heteroscedasticity
Distribution-free
Bootstrap Method
Test Statistic
Linear regression
Monte Carlo Simulation
Regression
Valid
Testing
Alternatives
Estimate

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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Approximate transformation trimmed mean methods to the test of simple linear regression slope equality. / Luh, Wei-Ming; Guo, Jiin Huarng.

In: Journal of Applied Statistics, Vol. 27, No. 7, 01.01.2000, p. 843-857.

Research output: Contribution to journalArticle

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