Permutation Predictor Tests in Linear Regression

Title & Authors
Permutation Predictor Tests in Linear Regression
Ryu, Hye Min; Woo, Min Ah; Lee, Kyungjin; Yoo, Jae Keun;

Abstract
To determine whether each coefficient is equal to zero or not, usual $\small{t}$-tests are a popular choice (among others) in linear regression to practitioners because all statistical packages provide the statistics and their corresponding $\small{p}$-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.
Keywords
Linear regression;non-normality;permutation test;small samples;
Language
English
Cited by
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