Open Access
17- https://doi.org/10.1007/s11222-013-9403-7
APPLE: approximate path for penalized likelihood estimators
- Jun 1, 2013
- Statistics and Computing
- Yi Yu +1 more
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.
- # Penalized Likelihood Estimators
- # Gene Expression Data Sets
- # High-dimensional Data Analysis
- # Convex Penalties
- # Coordinate-descent Algorithm
- # Well-known Packages
- # Results In Parameter Estimation
- # Variable Selection
- # Parameter Estimation
- # Variable Estimation
