A Two-Step Penalized Regression Method with Networked Predictors

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Abstract

Penalized regression incorporating prior dependency structure of predictors can be effective in high-dimensional data analysis (Li and Li in Bioinformatics, 24:1175-1118, 2008). Pan et al. (Biometrics, 66:474-484, 2010) proposed a penalized regression method for better outcome prediction and variable selection by smoothing parameters over a given predictor network, which can be applied to analysis of microarray data with a given gene network. In this paper, we develop two modifications to their method for further performance enhancement. First, we employ convex programming and show its improved performance over an approximate optimization algorithm implemented in their original proposal. Second, we perform bias reduction after initial variable selection through a new penalty, leading to better parameter estimates and outcome prediction. Simulations have demonstrated substantial performance improvement of the proposed modifications over the original method.

Original languageEnglish (US)
Pages (from-to)27-46
Number of pages20
JournalStatistics in Biosciences
Volume4
Issue number1
DOIs
StatePublished - May 2012

Bibliographical note

Funding Information:
Acknowledgements This research was supported by NIH grants R01GM081535, R01HL105397 and R01HL65462. We thank the reviewers for helpful comments.

Keywords

  • Fused Lasso
  • Gene networks
  • Group variable selection
  • L -norm
  • L -norm
  • Lasso
  • Microarray gene expression

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