  Screening methods are useful tools for variable selection in regression
analysis when the number of predictors is much larger than the sample size.
Factor analysis is used to eliminate multicollinearity among predictors, which
improves the variable selection performance. We propose a new method, called
Truncated Preconditioned Profiled Independence Screening (TPPIS), that better
selects 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. We confirmed the
superior performance of the proposed method in variable selection through
analysis using simulation data and real datasets.
