A variable selection method for detecting abnormality based on the T2 test. Issue 17 (2nd September 2017) - Shinozaki, N.; Iida, T.
ABSTRACT: This paper proposes a variable selection method for detecting abnormal items based on the T 2 test when the observations on abnormal items are available. Based on the unbiased estimates of the powers for all subsets of variables, the variable selection method selects the subset of variables that maximizes the power estimate. Since more than one subsets of variables maximize the power estimate frequently, the averaged p -value of the rejected items is used as a second criterion. Although the performance of the method depends on the sample size for the abnormal items and the true power values for all subsets of variables, numerical experiments show the effectiveness of the proposed method. Since normal and abnormal items are simulated using one-factor and two-factor models, basic properties of the power functions for the models are investigated.