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APPLE : approximate path for penalized likelihood estimators
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Yu, Yi and Feng, Yang (2014) APPLE : approximate path for penalized likelihood estimators. Statistics and Computing, 24 (5). pp. 803-819. doi:10.1007/s11222-013-9403-7 ISSN 0960-3174.
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Official URL: http://dx.doi.org/10.1007/s11222-013-9403-7
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.
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
Journal or Publication Title: | Statistics and Computing | ||||||
Publisher: | Springer | ||||||
ISSN: | 0960-3174 | ||||||
Official Date: | September 2014 | ||||||
Dates: |
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Volume: | 24 | ||||||
Number: | 5 | ||||||
Page Range: | pp. 803-819 | ||||||
DOI: | 10.1007/s11222-013-9403-7 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access |
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