• Publications
  • Influence
Regression Shrinkage and Selection via the Lasso
SUMMARY We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than aExpand
  • 29,210
  • 4095
  • PDF
Significance analysis of microarrays applied to the ionizing radiation response
Microarrays can measure the expression of thousands of genes to identify changes in expression between different biological states. Methods are needed to determine the significance of these changesExpand
  • 11,716
  • 1670
  • PDF
An Introduction to the Bootstrap
  • 29,802
  • 1177
The Elements of Statistical Learning
  • 12,784
  • 1069
  • PDF
Least angle regression
The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will beExpand
  • 7,802
  • 982
  • PDF
Statistical significance for genomewide studies
With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of featuresExpand
  • 7,942
  • 970
  • PDF
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, andExpand
  • 12,839
  • 941
  • PDF
Generalized Additive Models.
  • 3,813
  • 576
Sparse inverse covariance estimation with the graphical lasso.
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--theExpand
  • 3,642
  • 527
  • PDF
Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications
The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. AExpand
  • 9,683
  • 489
  • PDF