Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Permutation Predictor Tests in Linear Regression

  • Ryu, Hye Min (Clinical Research Coordination Center, National Cancer Center) ;
  • Woo, Min Ah (Korea Information and Communication Industry Institute) ;
  • Lee, Kyungjin (Department of Statistics, Ewha Womans University) ;
  • Yoo, Jae Keun (Department of Statistics, Ewha Womans University)
  • Received : 2013.02.19
  • Accepted : 2013.03.25
  • Published : 2013.03.31
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Abstract

To determine whether each coefficient is equal to zero or not, usual $t$-tests are a popular choice (among others) in linear regression to practitioners because all statistical packages provide the statistics and their corresponding $p$-values. Under smaller samples (especially with non-normal errors) the tests often fail to correctly detect statistical significance. We propose a permutation approach by adopting a sufficient dimension reduction methodology to overcome this deficit. Numerical studies confirm that the proposed method has potential advantages over the t-tests. In addition, data analysis is also presented.

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References

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