Robert Tibshirani

Generalized Additive Models.

An Introduction to the Bootstrap

This article presents bootstrap methods for estimation, using simple arguments. Minitab macros for implementing these methods are given.

Cell type–specific gene expression differences in complex tissues

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 significa...

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

The Elements of Statistical Learning

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 b...

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 ...

Empirical Bayes Analysis of a Microarray Experiment

AbstractMicroarrays are a novel technology that facilitates the simultaneous measurement of thousands of gene expression levels. A typical microarray experiment can produce mill...

Empirical bayes methods and false discovery rates for microarrays

Abstract In a classic two‐sample problem, one might use Wilcoxon's statistic to test for a difference between treatment and control subjects. The analogous microarray experiment...

The Elements of Statistical Learning

Regularization Paths for Generalized Linear Models via Coordinate Descent

We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nom...

Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)

Boosting is one of the most important recent developments in\nclassification methodology. Boosting works by sequentially applying a\nclassification algorithm to reweighted versi...

Linear Smoothers and Additive Models

We study linear smoothers and their use in building nonparametric regression models. In the first part of this paper we examine certain aspects of linear smoothers for scatterpl...

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 ...

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 whi...

Improvements on Cross-Validation: The 632+ Bootstrap Method

Abstract A training set of data has been used to construct a rule for predicting future responses. What is the error rate of this rule? This is an important question both for co...

THE LASSO METHOD FOR VARIABLE SELECTION IN THE COX MODEL

I propose a new method for variable selection and shrinkage in Cox's proportional hazards model. My proposal minimizes the log partial likelihood subject to the sum of the absol...

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Sparse Principal Component Analysis

Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear c...

Statistical Learning with Sparsity

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to ...

A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis

We present a penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix. We approximate the matrix X as circumflexX = sigma(k=1)(K) ...

Generalized Additive Models

Likelihood-based regression models such as the normal linear regression model and the linear logistic model, assume a linear (or some other parametric) form for the covariates $...

A plain man's guide to the proportional hazards model.