| Alternate document: Details Regularization Path Algorithms for Detecting Gene Interactions (06) Mee Young Park, Trevor Hastie |
Mee Young Park, Trevor Hastie  Home/Search
Context
Related
| stanford.edu/~hasti...glmpath.jrssb.pdf Cached: PS.gz PS PDF This document uses CoBlitz to cache paper downloads. If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
Image Update HelpPS.gz PS PDF From: Stanford.EDU/~hastie//Papers/ (more) (Enter author homepages) |
Rate this article:      (best)Comment on this article |
Abstract: In this study, we introduce a path-following algorithm for L 1 regularized generalized linear models. The L 1 regularization procedure is useful especially because it, in e#ect, selects variables according to the amount of penalization on the L 1 norm of the coe#cients, in a manner less greedy than forward selection/backward deletion. (Update)
Active bibliography (related documents): More All
1.9: Regularization Path Algorithms for Detecting Gene Interactions - Park, Hastie (2006) (Correct)
0.9: Forward Stagewise Regression and the Monotone Lasso - Hastie, Taylor, Tibshirani, .. (2006) (Correct)
0.5: On Model Selection Consistency of Lasso - Zhao, Yu (2006) (Correct)
Similar documents based on text:
6.0: Unknown - (Correct)
BibTeX entry: (Update)
@misc{ park-regularization,
author = "Mee Young Park and Trevor Hastie",
title = "L_1 Regularization Path Algorithm for Generalized Linear Models",
url = "citeseer.ist.psu.edu/park06regularization.html" }
Citations (may not include all citations):546 An Introduction to the Bootstrap (context) - Efron, Tibshirani - 1993
520 Generalized Linear Models (context) - McCullagh, Nelder - 1989
165 Molecular classification of cancer: Class discovery and clas.. (context) - Golub, Slonim et al. - 1999
118 Regression models and life-tables (context) - Cox - 1972
94 Elements of Statistical Learning; Data Mining (context) - Hastie, Tibshirani et al. - 2001
83 Regression shrinkage and selection via the lasso - Tibshirani - 1996
77 Numerical Continuation Methods (context) - Allgower, Georg - 1990
66 Estimation of the mean of a multivariate normal distribution (context) - Stein - 1981
24 Pathways to Solutions (context) - Garcia, Zangwill - 1981
20 Least angle regression - Efron, Hastie et al. - 2004
17 Analysis on Manifolds (context) - Munkres - 2000
9 Boosting as a regularized path to a maximum margin classifie.. - Rosset, Zhu et al. - 2004
8 Large-scale bayesian logistic regression for text categoriza.. (context) - Genkin, Lewis et al. - 2004
6 The lasso method for variable selection in the cox model (context) - Tibshirani - 1997
6 The entire regularization path for the support vector machin.. - Hastie, Rosset et al. - 2004
6 Bias reduction of maximum likelihood estimates (context) - Firth - 1993
4 norm support vector machines - Zhu, Rosset et al. - 2003
3 Tracking curved regularized optimization solution paths (context) - Rosset - 2004
2 Boosted lasso (context) - Zhao, Yu - 2004
2 Covariance analysis of heart transplant survival data (context) - Crowley, Hu - 1977
2 degrees of freedom (context) - Zou, Hastie - 2004
2 Piecewise linear regularized solution paths (context) - Rosset, Zhu - 2003
2 Regularization and variable selection via the elastic net (context) - Zou, Hastie - 2005
1 The lasso and generalised linear models (context) - Lokhorst - 1999
1 A simple and e#cient algorithm for gene selection using spar.. (context) - Shevade, Keerthi - 2003
1 A solution to the problem of separation in logistic regressi.. (context) - Heinze, Schemper - 2002
Documents on the same site (http://www-stat.Stanford.EDU/~hastie//Papers/): More
Practical Procedures for Dimension Reduction in l_1 - Li, Hastie, Church (Correct)
The Entire Regularization Path for the Support Vector.. - Hastie, Rosset.. (2004) (Correct)
Constrained Ordination Analysis with Flexible Response.. - Zhu, Hastie, Walther (2005) (Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC