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ELASTIC NET FOR COX'S PROPORTIONAL HAZARDS MODEL WITH A SOLUTION PATH ALGORITHM.

Yichao Wu1

  • 1Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. wu@stat.ncsu.edu.

Statistica Sinica
|December 11, 2012
PubMed
Summary
This summary is machine-generated.

This study extends the least angle regression (LAR) algorithm to develop a solution path for the elastic net penalty in Cox

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Area of Science:

  • Survival analysis
  • Statistical modeling
  • Computational statistics

Background:

  • Least Angle Regression (LAR) and LASSO are efficient algorithms for linear models.
  • Existing methods have been extended to generalized linear models.
  • Cox's proportional hazards model is widely used for survival data analysis.

Purpose of the Study:

  • To develop a solution path algorithm for the elastic net penalty in Cox's proportional hazards model.
  • To extend the Least Angle Regression (LAR) algorithm to handle Cox models.
  • To provide an efficient computational method for high-dimensional survival data analysis.

Main Methods:

  • Extension of the Least Angle Regression (LAR) algorithm.
  • Optimization of the log partial likelihood with a ridge term.
  • Path modification for elastic net regularization.
  • Utilizing ordinary differential equation systems for solution path determination.

Main Results:

  • An exact and piecewise solution path algorithm for the elastic net penalty in Cox's proportional hazards model.
  • Successful extension of LAR to Cox models.
  • Demonstration of the algorithm's capability to handle complex survival data.

Conclusions:

  • The developed algorithm provides an efficient method for fitting elastic net regularized Cox models.
  • This work bridges the gap between penalized regression methods and survival analysis.
  • The approach offers a valuable tool for feature selection and prediction in high-dimensional survival data.