Multi-stage Sparse Variable Selection Algorithm for High-dimensional Regression
Aiming at the variable selection problem of microarray data in high-dimensional linear model,es-pecially when the number of independent variables for exceeds the number of samples,a multi-stage varia-ble selection algorithm was proposed.The algorithm is based on thresholded elastic net regularization method,combined with step-by-step multiple hypothesis testing.It enabled variable selection at multiple stages while ensuring sparsity and prediction accuracy.Results from simulation data and empirical study demonstrate that the algorithm performs excellently with finite samples,effectively recovering the true model and significantly reducing the number of false positive variables,thereby maintaining prediction ac-curacy.
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