APPLE: approximate path for penalized likelihood estimators

被引：6

作者：

Yu, Yi ^{[1
]}

Feng, Yang ^{[2
]}

机构：

[1] Fudan Univ, Sch Math Sci, Shanghai 200433, Peoples R China

[2] Columbia Univ, Dept Stat, New York, NY 10027 USA

来源：

STATISTICS AND COMPUTING | 2014年 / 24卷 / 05期

基金：

美国国家科学基金会;

关键词：

APPLE; LASSO; MCP; Penalized likelihood estimator; Solution path; COORDINATE DESCENT ALGORITHMS; GENERALIZED LINEAR-MODELS; VARIABLE SELECTION; LOGISTIC-REGRESSION; LASSO; CLASSIFICATION; SPARSITY;

D O I：

10.1007/s11222-013-9403-7

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：803 / 819

页数：17

共 40 条

[1] [Anonymous], 1989, GEN LINEAR MODEL
[2] [Anonymous], 1973, Inst Statist Math
[3] Risk bounds for model selection via penalization
Barron, A
Birgé, L
Massart, P
[J]. PROBABILITY THEORY AND RELATED FIELDS, 1999, 113 (03) : 301 - 413
[4] COORDINATE DESCENT ALGORITHMS FOR NONCONVEX PENALIZED REGRESSION, WITH APPLICATIONS TO BIOLOGICAL FEATURE SELECTION
Breheny, Patrick
Huang, Jian
[J]. ANNALS OF APPLIED STATISTICS, 2011, 5 (01) : 232 - 253
[5] Understanding WaveShrink: Variance and bias estimation
Bruce, AG
Gao, HY
[J]. BIOMETRIKA, 1996, 83 (04) : 727 - 745
[6] Extended Bayesian information criteria for model selection with large model spaces
Chen, Jiahua
Chen, Zehua
[J]. BIOMETRIKA, 2008, 95 (03) : 759 - 771
[7] CHEN S., 1994, BASIS PURSUIT
[8] An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
Daubechies, I
Defrise, M
De Mol, C
[J]. COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS, 2004, 57 (11) : 1413 - 1457
[9] Least angle regression - Rejoinder
Efron, B
Hastie, T
Johnstone, I
Tibshirani, R
[J]. ANNALS OF STATISTICS, 2004, 32 (02) : 494 - 499
[10] Fan J., 2012, ARXIV12105992

← 1 2 3 4 →