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.

翻译：筛选方法是在预测变量数量远大于样本量时，回归分析中用于变量选择的有用工具。因子分析可消除预测变量间的多重共线性，从而提升变量选择性能。我们提出一种新方法——截断预处理剖面独立筛选（Truncated Preconditioned Profiled Independence Screening, TPPIS），该方法能更优地选择因子数量以消除多重共线性。所提方法通过截断因子分析所得信息中的非必要部分，改进了变量选择性能。通过模拟数据与真实数据集的实证分析，我们验证了所提方法在变量选择中的优越性能。