Robert Tibshirani

Dados Biográficos

ID3448255
NOMERobert Tibshirani
PRENOMESRobert
SOBRENOMETibshirani
ASSINATURATIBSHIRANI R
AFILIAÇÕESStanford University
ORCID0000-0003-0553-5090
VERIFICADOSim
TOTAL DE OBRAS12
TOTAL DE CITAÇÕES0
TOTAL COMO AUTOR12
TOTAL COMO EDITOR0
PRIMEIRO ANO DE PUBLICAÇÃO1984
ANO MAIS RECENTE DE PUBLICAÇÃO2015
ÍNDICE H0

  • Resampling Methods of Estimation

    Open AccessBradley Efron, David Rogosa et al.CHAPTERInternational Encyclopedia of the…2015

  • Regression Shrinkage and Selection via The Lasso

    Open AccessRobert TibshiraniARTICLEJournal of the Royal Statistical…2011

    In the paper I give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and selection via the lasso.

  • Regularization Paths for Generalized Linear Models via Coordinate Descent

    Open AccessJerome Friedman, Jerome H Friedman et al.ARTICLEJournal of Statistical Software2010

    We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include l 1 (the lasso), l 2 (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also d…

  • Sparse inverse covariance estimation with the graphical lasso

    Jerome Friedman, Jerome H Friedman et al.ARTICLEBiostatistics2008

    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—the graphical lasso—that is remarkably fast: It solves a 1000-node problem (∼500000 parameters) in at most a minute and is 30–4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation sugg…

  • Least angle regression

    Bradley Efron, Trevor Hastie et al.ARTICLEThe Annals of Statistics2004

    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 be applied. Typically we have available a large collection of possible covariates from which we hope to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regression (LARS), a new model selection algorithm, is a use…

  • Estimating the Number of Clusters in a Data Set Via the Gap Statistic

    Open AccessRobert Tibshirani, Guenther Walther et al.ARTICLEJournal of the Royal Statistical…2001

    We propose a method (the ‘gap statistic’) for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. K-means or hierarchical), comparing the change in within-cluster dispersion with that expected under an appropriate reference null distribution. Some theory is developed for the proposal and a simulation study shows that the gap statistic usually outperforms other methods that h…

  • Additive logistic regression

    Jerome Friedman, Jerome H Friedman et al.ARTICLEThe Annals of Statistics2000

    Boosting is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data and then taking a weighted majority vote of the sequence of classifiers thus produced. For many classification algorithms, this simple strategy results in dramatic improvements in performance. We show that this seemingly mysterious phenomenon can be …

  • Regression Shrinkage and Selection Via the Lasso

    Open AccessRobert TibshiraniARTICLEJournal of the Royal Statistical…1996

    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 a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ri…

  • An Introduction to the Bootstrap

    Bradley Efron, Robert Tibshirani et al.BOOKIntroduction to the Bootstrap1994

    An Introduction to the Bootstrap arms scientists and engineers as well as statisticians with the computational techniques they need to analyze and understand complicated data sets. The bootstrap is a computer-based method of statistical inference that answers statistical questions without formulas and gives a direct appreciation of variance, bias, coverage, and other probabilistic phenomena. This book presents an overview of the bootstrap and rel…

  • Varying-Coefficient Models

    Open AccessTrevor Hastie, Robert TibshiraniARTICLEJournal of the Royal Statistical…1993

    We explore a class of regression and generalized regression models in which the coefficients are allowed to vary as smooth functions of other variables. General algorithms are presented for estimating the models flexibly and some examples are given. This class of models ties together generalized additive models and dynamic generalized linear models into one common framework. When applied to the proportional hazards model for survival data, this a…

  • Generalized Additive Models

    Trevor Hastie, Robert TibshiraniARTICLEStatistical Science1986

    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 $X_1, X_2, \cdots, X_p$. We introduce the class of generalized additive models which replaces the linear form $\sum \beta_jX_j$ by a sum of smooth functions $\sum s_j(X_j)$. The $s_j(\cdot)$'s are unspecified functions that are estimated using a scatterplot smoother, in an …

  • Describing Health States

    Hilary Llewellyn-Thomas, Hilary A Llewellyn‐Thomas et al.ARTICLEMedical Care1984

    In health status index construction quantitative values for different states of health are frequently obtained by presenting written descriptions to raters whose values are elicited using one or more methods. In this study the authors examined the influence of several aspects of this measurement process upon the quantitative results obtained. They prepared a set of written descriptions of health states, each state being described in both a standa…