WorldCat Identities

Tibshirani, Robert

Overview
Works: 114 works in 333 publications in 2 languages and 4,228 library holdings
Roles: Author, Thesis advisor, Contributor
Classifications: Q325.75, 519.544
Publication Timeline
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Most widely held works by Robert Tibshirani
An introduction to the bootstrap by Bradley Efron( Book )

33 editions published between 1993 and 1998 in English and held by 1,067 WorldCat member libraries worldwide

The accuracy of a simple mean - Random simples and probabilities - The empirical distribution function and the plug-in 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 - Cross-validation 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 - Further topics in bootstrap confidence intervals - Efficient bootstrap computations - Approximate likelihoods - Bootstrap bioequivalence - Discussion and further topics
The elements of statistical learning : data mining, inference, and prediction by Trevor Hastie( Book )

58 editions published between 2001 and 2013 in English and held by 854 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
The elements of statistical learning : data mining, inference, and prediction : with 200 full-color illustrations by Trevor Hastie( Book )

8 editions published between 2001 and 2004 in English and held by 575 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 )

40 editions published between 1984 and 1999 in English and Undetermined and held by 566 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 likelihood-based 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 non-standard models like Cox's proportional hazards model for survival data
An introduction to statistical learning : with applications in R by Gareth James( Book )

9 editions published between 2013 and 2014 in English and held by 209 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, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. Provides tools for Statistical Learning that are essential for practitioners in science, industry and other fields. Analyses and methods are presented in R. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering. Extensive use of color graphics assist the reader"--Publisher description
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

Statistical learning with sparsity : the lasso and generalizations by Trevor Hastie( Book )

11 editions published in 2015 in English and Dutch and held by 46 WorldCat member libraries worldwide

Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ1 penalties to generalized l
Local likelihood estimation by Robert Tibshirani( Book )

8 editions published between 1984 and 1986 in English and held by 7 WorldCat member libraries worldwide

Bootstrap confidence intervals and bootstrap approximations by Thomas J DiCiccio( Book )

3 editions published between 1985 and 1986 in English and held by 4 WorldCat member libraries worldwide

This document studies the BC sub a bootstrap procedure for constructing parametric and non-parametric confidence intervals. The BC sub a interval relies on the existence of a transformation that maps the problem into a normal scaled transformation family. The authors show how to construct this transformation in general. Exploiting this, they derive an interval that equals the BC sub a interval to second order, computable without bootstrap sampling. As a further benefit, this construction provides a second order correct approximation to the bootstrap distribution of a statistic, computed without bootstrap sampling. Both the new interval and the approximation require only n+2 evaluations of the statistic, where n is the sample size. (Author)
Bootstrap Confidence Intervals by Robert Tibshirani( Book )

6 editions published in 1984 in English and held by 4 WorldCat member libraries worldwide

We describe the various techniques that were proposed for constructing non-parametric confidence intervals using the bootstrap. These include bootstrap pivotal intervals, percentile and bias-corrected percentile intervals, and non-parametric 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 non-parametric 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
Cross-validation 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 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 3 WorldCat member libraries worldwide

The problem of regions by Bradley Efron( Book )

4 editions published in 1997 in English and held by 3 WorldCat member libraries worldwide

Pre-validation and inference in microarrays by Robert Tibshirani( Book )

4 editions published in 2002 in English and held by 3 WorldCat member libraries worldwide

Bayesian backfitting by Trevor Hastie( Book )

4 editions published in 1998 in English and held by 3 WorldCat member libraries worldwide

Computer-intensive statistical methods by Bradley Efron( Book )

3 editions published in 1995 in English and held by 3 WorldCat member libraries worldwide

Additive logistic regression : a statistical view of boosting by J. H Friedman( Book )

3 editions published in 1998 in English and held by 3 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 3 WorldCat member libraries worldwide

Discriminant adaptive nearest neighbor classification by Trevor Hastie( Book )

4 editions published in 1994 in English and held by 2 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 2 WorldCat member libraries worldwide

 
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An introduction to the bootstrap
Alternative Names
Robert Tibshirani statisticien canado-amricain

Robert Tibshirani statistico statunitense

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. 1956-...

Tibshirani, Robert John 1956-

Роберт Тибширани

Languages
English (220)

Dutch (1)

Covers
The elements of statistical learning : data mining, inference, and predictionThe elements of statistical learning : data mining, inference, and prediction : with 200 full-color illustrationsGeneralized additive modelsThe science of Bradley Efron : selected papers