B1436
Title: Variable screening using factor analysis for high-dimensional data with multicollinearity
Authors: Shuntaro Tanaka - Shiga University (Japan) [presenting]
Hidetoshi Matsui - Shiga University (Japan)
Abstract: Screening methods are useful tools for variable selection in regression analysis when the number of predictors is much larger than the sample size. However, when predictors have multicollinearity, it is often difficult to select variables appropriately. Factor analysis is used to eliminate multicollinearity among predictors, which improves the variable selection performance. A method is proposed to select the number of factors to eliminate multicollinearity. The proposed method improves the variable selection performance by truncating unnecessary parts from the information obtained by factor analysis. The performance of the proposed method is confirmed through analysis using simulation data and real datasets.
