Tibshirani, Robert
Overview
Works:  103 works in 297 publications in 1 language and 3,801 library holdings 

Roles:  Author, Thesis advisor 
Classifications:  Q325.75, 519.544 
Publication Timeline
.
Most widely held works by
Robert Tibshirani
The elements of statistical learning : data mining, inference, and prediction
by
Trevor Hastie(
Book
)
48 editions published between 2001 and 2013 in English and held by 1,183 WorldCat member libraries worldwide
"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, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics."Jacket
48 editions published between 2001 and 2013 in English and held by 1,183 WorldCat member libraries worldwide
"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, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics."Jacket
An introduction to the bootstrap
by
Bradley Efron(
Book
)
32 editions published between 1993 and 1998 in English and held by 1,047 WorldCat member libraries worldwide
The accuracy of a sample mean; Random samples and probabilities; The empirical distribution function and the plugin principle; Standard errors and estimated standard errors; The bootstrap estimate of standard error; Bootstrap standard errors: some examples; More complicated data structures; Regression models; Estimates of bias; The jackknife; Confidence intervals based on bootstrap "tables"; Confidence intervals based on bootstrap percentiles; Better bootstrap confidence intervals; Permutation tests; Hypothesis testing with the bootstrap; Crossvalidation and other estimates of prediction error; Adaptive estimation and calibration; Assessing the error in bootstrap estimates; A geometrical representation for the bootstrap and jackknife; An overview of nonparametric and parametric inference; Furter topics in bootstrap confidence intervals; Efficient bootstrap computatios; Approximate likelihoods; Bootstrap bioequivalence; Discussion and further topics
32 editions published between 1993 and 1998 in English and held by 1,047 WorldCat member libraries worldwide
The accuracy of a sample mean; Random samples and probabilities; The empirical distribution function and the plugin principle; Standard errors and estimated standard errors; The bootstrap estimate of standard error; Bootstrap standard errors: some examples; More complicated data structures; Regression models; Estimates of bias; The jackknife; Confidence intervals based on bootstrap "tables"; Confidence intervals based on bootstrap percentiles; Better bootstrap confidence intervals; Permutation tests; Hypothesis testing with the bootstrap; Crossvalidation and other estimates of prediction error; Adaptive estimation and calibration; Assessing the error in bootstrap estimates; A geometrical representation for the bootstrap and jackknife; An overview of nonparametric and parametric inference; Furter topics in bootstrap confidence intervals; Efficient bootstrap computatios; Approximate likelihoods; Bootstrap bioequivalence; Discussion and further topics
The elements of statistical learning : data mining, inference, and prediction : with 200 fullcolor illustrations
by
Trevor Hastie(
Book
)
9 editions published between 2001 and 2004 in English and held by 601 WorldCat member libraries worldwide
Describes important statistical ideas in machine learning, data mining, and bioinformatics. Covers a broad range, from supervised learning (prediction), to unsupervised learning, including classification trees, neural networks, and support vector machines
9 editions published between 2001 and 2004 in English and held by 601 WorldCat member libraries worldwide
Describes important statistical ideas in machine learning, data mining, and bioinformatics. Covers a broad range, from supervised learning (prediction), to unsupervised learning, including classification trees, neural networks, and support vector machines
Generalized additive models
by
Trevor Hastie(
Book
)
38 editions published between 1984 and 1999 in English and Undetermined and held by 546 WorldCat member libraries worldwide
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 covariate effects. The authors introduce the Local Scoring procedure which is applicable to any likelihoodbased regression model: the class of Generalized Linear Models contains many of these. In this class the Local Scoring procedure replaces a linear predictor by a additive predictor; hence the name Generalized Additive Models. Local Scoring can also be applied to nonstandard models like Cox's proportional hazards model for survival data
38 editions published between 1984 and 1999 in English and Undetermined and held by 546 WorldCat member libraries worldwide
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 covariate effects. The authors introduce the Local Scoring procedure which is applicable to any likelihoodbased regression model: the class of Generalized Linear Models contains many of these. In this class the Local Scoring procedure replaces a linear predictor by a additive predictor; hence the name Generalized Additive Models. Local Scoring can also be applied to nonstandard models like Cox's proportional hazards model for survival data
An introduction to statistical learning with applications in R
by
Gareth James(
Book
)
4 editions published between 2013 and 2014 in English and held by 159 WorldCat member libraries worldwide
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, treebased methods, support vector machines, clustering, and more. Color graphics and realworl
4 editions published between 2013 and 2014 in English and held by 159 WorldCat member libraries worldwide
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, treebased methods, support vector machines, clustering, and more. Color graphics and realworl
The science of Bradley Efron : selected papers
by
Bradley Efron(
Book
)
7 editions published between 2008 and 2011 in English and held by 92 WorldCat member libraries worldwide
7 editions published between 2008 and 2011 in English and held by 92 WorldCat member libraries worldwide
Local likelihood estimation
by
Robert Tibshirani(
Book
)
8 editions published between 1984 and 1986 in English and held by 8 WorldCat member libraries worldwide
8 editions published between 1984 and 1986 in English and held by 8 WorldCat member libraries worldwide
Crossvalidation and the bootstrap: estimating the error rate of a prediction rule
by
Bradley Efron(
Book
)
5 editions published in 1995 in English and held by 6 WorldCat member libraries worldwide
5 editions published in 1995 in English and held by 6 WorldCat member libraries worldwide
Statistical data analysis in the computer age
by
Bradley Efron(
Book
)
5 editions published between 1990 and 1991 in English and held by 5 WorldCat member libraries worldwide
5 editions published between 1990 and 1991 in English and held by 5 WorldCat member libraries worldwide
Bootstrap confidence intervals
by
Robert Tibshirani(
Book
)
5 editions published in 1984 in English and held by 5 WorldCat member libraries worldwide
We describe the various techniques that were proposed for constructing nonparametric confidence intervals using the bootstrap. These include bootstrap pivotal intervals, percentile and biascorrected percentile intervals, and nonparametric titling intervals. These methods are small sample improvements over the usual + or  standard deviation intervals. We discuss them in detail, outlining underlying assumptions in each case. We show how the nonparametric titling interval can be viewed as an extension of a bootstrap pivotal interval, and suggest a number of generalizations. Finally, the various intervals are compared in a small simulation study
5 editions published in 1984 in English and held by 5 WorldCat member libraries worldwide
We describe the various techniques that were proposed for constructing nonparametric confidence intervals using the bootstrap. These include bootstrap pivotal intervals, percentile and biascorrected percentile intervals, and nonparametric titling intervals. These methods are small sample improvements over the usual + or  standard deviation intervals. We discuss them in detail, outlining underlying assumptions in each case. We show how the nonparametric titling interval can be viewed as an extension of a bootstrap pivotal interval, and suggest a number of generalizations. Finally, the various intervals are compared in a small simulation study
The problem of regions
by
Bradley Efron(
Book
)
4 editions published in 1997 in English and held by 5 WorldCat member libraries worldwide
4 editions published in 1997 in English and held by 5 WorldCat member libraries worldwide
Prevalidation and inference in microarrays
by
Robert Tibshirani(
Book
)
4 editions published in 2002 in English and held by 4 WorldCat member libraries worldwide
4 editions published in 2002 in English and held by 4 WorldCat member libraries worldwide
How many bootstraps?
by
Stanford University(
Book
)
3 editions published in 1985 in English and held by 4 WorldCat member libraries worldwide
The bootstrap is a nonparametric method for assessing statistical accuracy. In approximating bootstrap quantities by monte carlo simulation, one must decide how many bootstrap samples to generate. This document proposes an adaptive sequential method that estimates the accuracy based on the current bootstrap samples. Bootstrap sampling is continued until the estimated accuracy is high enough. In the examples given, 100 to 300 bootstraps are sufficient for standard error and bias estimation, while 1000 bootstraps may be necessary for estimating a percentile. Additional keywords: Tables(data)
3 editions published in 1985 in English and held by 4 WorldCat member libraries worldwide
The bootstrap is a nonparametric method for assessing statistical accuracy. In approximating bootstrap quantities by monte carlo simulation, one must decide how many bootstrap samples to generate. This document proposes an adaptive sequential method that estimates the accuracy based on the current bootstrap samples. Bootstrap sampling is continued until the estimated accuracy is high enough. In the examples given, 100 to 300 bootstraps are sufficient for standard error and bias estimation, while 1000 bootstraps may be necessary for estimating a percentile. Additional keywords: Tables(data)
Additive logistic regression : a statistical view of boosting
by
J. H Friedman(
Book
)
3 editions published in 1998 in English and held by 4 WorldCat member libraries worldwide
3 editions published in 1998 in English and held by 4 WorldCat member libraries worldwide
Bayesian backfitting
by
Trevor Hastie(
Book
)
4 editions published in 1998 in English and held by 4 WorldCat member libraries worldwide
4 editions published in 1998 in English and held by 4 WorldCat member libraries worldwide
The Elements of Statistical Learning
by
Trevor Hastie(
)
2 editions published in 2009 in English and held by 4 WorldCat member libraries worldwide
Covers supervised learning (prediction) to unsupervised learning. This book contains topics including neural networks, support vector machines, classification trees and boosting
2 editions published in 2009 in English and held by 4 WorldCat member libraries worldwide
Covers supervised learning (prediction) to unsupervised learning. This book contains topics including neural networks, support vector machines, classification trees and boosting
Computerintensive statistical methods
by
Bradley Efron(
Book
)
3 editions published in 1995 in English and held by 4 WorldCat member libraries worldwide
3 editions published in 1995 in English and held by 4 WorldCat member libraries worldwide
Microarrays empirical Bayes methods, and false discovery rates
by
Bradley Efron(
Book
)
3 editions published in 2001 in English and held by 4 WorldCat member libraries worldwide
3 editions published in 2001 in English and held by 4 WorldCat member libraries worldwide
Using specially designed exponential families for density estimation
by
Bradley Efron(
Book
)
4 editions published in 1994 in English and held by 4 WorldCat member libraries worldwide
4 editions published in 1994 in English and held by 4 WorldCat member libraries worldwide
Discriminant adaptive nearest neighbor classification
by
Trevor Hastie(
)
4 editions published in 1994 in English and held by 4 WorldCat member libraries worldwide
4 editions published in 1994 in English and held by 4 WorldCat member libraries worldwide
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Related Identities
 Hastie, Trevor Thesis advisor Author
 Friedman, J. H. (Jerome H.) Thesis advisor Author
 Efron, Bradley Speaker Thesis advisor Author
 Witten, Daniela
 James, Gareth (Gareth Michael) Author
 Morris, Carl N.
 Hastie, Trevor J. Author
 Stanford University Department of Statistics
 United States Public Health Service
 Stanford University Division of Biostatistics
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Associated Subjects
Artificial intelligence Asymptotic efficiencies (Statistics) Bayesian statistical decision theory Bioinformatics BiologyData processing Bootstrap (Statistics) Computational biology Computational intelligence Computers Computer science Data mining Electronic data processing Estimation theory Forecasting Inference Linear models (Statistics) Machine learning Mathematical models Mathematical statistics MathematicsData processing R (Computer program language) Random walks (Mathematics) Regression analysis Sampling (Statistics) Smoothing (Statistics) Statistics StatisticsMethodology Supervised learning (Machine learning)
Alternative Names
Tibshirani, R. J.
Tibshirani, R. J. 1956
Tibshirani, R. J. (Robert J.)
Tibshirani, R. J. (Robert J.) 1956
Tibshirani, Rob.
Tibshirani, Rob J.
Tibshirani, Robert.
Tibshirani, Robert J.
Tibshirani, Robert J. 1956...
Tibshirani, Robert John 1956
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