APPLE: Approximate path for penalized likelihood estimators

Yi Yu, Yang Feng

Research output: Contribution to journalArticlepeer-review

Abstract

In high-dimensional data analysis, penalized likelihood estimators are shown to provide superior results in both variable selection and parameter estimation. A new algorithm, APPLE, is proposed for calculating the Approximate Path for Penalized Likelihood Estimators. Both convex penalties (such as LASSO) and folded concave penalties (such as MCP) are considered. APPLE efficiently computes the solution path for the penalized likelihood estimator using a hybrid of the modified predictor-corrector method and the coordinate-descent algorithm. APPLE is compared with several well-known packages via simulation and analysis of two gene expression data sets.

Original languageEnglish (US)
Pages (from-to)803-819
Number of pages17
JournalStatistics and Computing
Volume24
Issue number5
DOIs
StatePublished - Sep 2014

Keywords

  • APPLE
  • LASSO
  • MCP
  • Penalized likelihood estimator
  • Solution path

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

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