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
收藏
页码：803 / 819
页数：17
相关论文
共 40 条
  • [21] Sparse multinomial logistic regression: Fast algorithms and generalization bounds
    Krishnapuram, B
    Carin, L
    Figueiredo, MAT
    Hartemink, AJ
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2005, 27 (06) : 957 - 968
  • [22] Lee Su-In., 2006, Proceedings of the National Conference on Artificial Intelligence, V21, P401
  • [23] SOME COMMENTS ON CP
    MALLOWS, CL
    [J]. TECHNOMETRICS, 1973, 15 (04) : 661 - 675
  • [24] The group lasso for logistic regression
    Meier, Lukas
    van de Geer, Sara A.
    Buhlmann, Peter
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2008, 70 : 53 - 71
  • [25] A new approach to variable selection in least squares problems
    Osborne, MR
    Presnell, B
    Turlach, BA
    [J]. IMA JOURNAL OF NUMERICAL ANALYSIS, 2000, 20 (03) : 389 - 403
  • [26] L1-regularization path algorithm for generalized linear models
    Park, Mee Young
    Hastie, Trevor
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2007, 69 : 659 - 677
  • [27] Piecewise linear regularized solution paths
    Rosset, Saharon
    Zhu, Ji
    [J]. ANNALS OF STATISTICS, 2007, 35 (03) : 1012 - 1030
  • [28] ESTIMATING DIMENSION OF A MODEL
    SCHWARZ, G
    [J]. ANNALS OF STATISTICS, 1978, 6 (02) : 461 - 464
  • [29] A simple and efficient algorithm for gene selection using sparse logistic regression
    Shevade, SK
    Keerthi, SS
    [J]. BIOINFORMATICS, 2003, 19 (17) : 2246 - 2253
  • [30] The MicroArray Quality Control (MAQC)-IIII study of common practices for the development and validation of microarray-based predictive models
    Shi, Leming
    Campbell, Gregory
    Jones, Wendell D.
    Campagne, Fabien
    Wen, Zhining
    Walker, Stephen J.
    Su, Zhenqiang
    Chu, Tzu-Ming
    Goodsaid, Federico M.
    Pusztai, Lajos
    Shaughnessy, John D., Jr.
    Oberthuer, Andre
    Thomas, Russell S.
    Paules, Richard S.
    Fielden, Mark
    Barlogie, Bart
    Chen, Weijie
    Du, Pan
    Fischer, Matthias
    Furlanello, Cesare
    Gallas, Brandon D.
    Ge, Xijin
    Megherbi, Dalila B.
    Symmans, W. Fraser
    Wang, May D.
    Zhang, John
    Bitter, Hans
    Brors, Benedikt
    Bushel, Pierre R.
    Bylesjo, Max
    Chen, Minjun
    Cheng, Jie
    Cheng, Jing
    Chou, Jeff
    Davison, Timothy S.
    Delorenzi, Mauro
    Deng, Youping
    Devanarayan, Viswanath
    Dix, David J.
    Dopazo, Joaquin
    Dorff, Kevin C.
    Elloumi, Fathi
    Fan, Jianqing
    Fan, Shicai
    Fan, Xiaohui
    Fang, Hong
    Gonzaludo, Nina
    Hess, Kenneth R.
    Hong, Huixiao
    Huan, Jun
    [J]. NATURE BIOTECHNOLOGY, 2010, 28 (08) : 827 - U109
1234