APPLE: approximate path for penalized likelihood estimators. 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.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Diego Saldana; Yang Feng: SIS: An R Package for Sure Independence Screening in Ultrahigh-Dimensional Statistical Models (2018) not zbMATH
- Hao, Ning; Feng, Yang; Zhang, Hao Helen: Model selection for high-dimensional quadratic regression via regularization (2018)
- Shi, Yue Yong; Jiao, Yu Ling; Cao, Yong Xiu; Liu, Yan Yan: An alternating direction method of multipliers for MCP-penalized regression with high-dimensional data (2018)
- Yu, Yi; Feng, Yang: APPLE: approximate path for penalized likelihood estimators (2014)