Hastie, Trevor
Overview
Works:  70 works in 317 publications in 3 languages and 4,156 library holdings 

Genres:  History 
Roles:  Author, Other, Thesis advisor, Editor 
Classifications:  Q325.75, 006.31 
Publication Timeline
.
Most widely held works by
Trevor Hastie
The elements of statistical learning : data mining, inference, and prediction : with 200 fullcolor illustrations by
Trevor Hastie(
Book
)
91 editions published between 2001 and 2017 in English and held by 1,618 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
91 editions published between 2001 and 2017 in English and held by 1,618 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
)
42 editions published between 1984 and 1999 in English and Undetermined and held by 587 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
42 editions published between 1984 and 1999 in English and Undetermined and held by 587 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
)
17 editions published between 2013 and 2015 in English and held by 361 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 realworld 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 cowrote 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 nonstatisticians alike who wish to use cuttingedge 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, treebased methods, support vector machines, and clustering. Extensive use of color graphics assist the reader"Publisher description
17 editions published between 2013 and 2015 in English and held by 361 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 realworld 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 cowrote 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 nonstatisticians alike who wish to use cuttingedge 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, treebased methods, support vector machines, and clustering. Extensive use of color graphics assist the reader"Publisher description
Statistical models in S by
John M Chambers(
Book
)
34 editions published between 1991 and 1999 in English and Japanese and held by 354 WorldCat member libraries worldwide
34 editions published between 1991 and 1999 in English and Japanese and held by 354 WorldCat member libraries worldwide
Computer age statistical inference : algorithms, evidence, and data science by
Bradley Efron(
Book
)
12 editions published in 2016 in English and held by 193 WorldCat member libraries worldwide
The twentyfirst century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories  Bayesian, frequentist, Fisherian  individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.  Provided by publisher
12 editions published in 2016 in English and held by 193 WorldCat member libraries worldwide
The twentyfirst century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories  Bayesian, frequentist, Fisherian  individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.  Provided by publisher
Statistical learning with sparsity : the lasso and generalizations by
Trevor Hastie(
Book
)
19 editions published in 2015 in 3 languages and held by 119 WorldCat member libraries worldwide
Discover New Methods for Dealing with HighDimensional Data A 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 linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and uptodate treatment of sparse statistical modeling
19 editions published in 2015 in 3 languages and held by 119 WorldCat member libraries worldwide
Discover New Methods for Dealing with HighDimensional Data A 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 linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and uptodate treatment of sparse statistical modeling
Tōkeiteki gakushū no kiso : dēta mainingu suiron yosoku(
Book
)
2 editions published in 2014 in Japanese and held by 6 WorldCat member libraries worldwide
2 editions published in 2014 in Japanese and held by 6 WorldCat member libraries worldwide
Estimation of additive principal components using penalized least squares : implementation in S by
Andreas Buja(
Book
)
1 edition published in 1992 in English and held by 4 WorldCat member libraries worldwide
1 edition published in 1992 in English and held by 4 WorldCat member libraries worldwide
Principal curves and surfaces by
Trevor Hastie(
Book
)
6 editions published between 1984 and 1985 in English and held by 4 WorldCat member libraries worldwide
Principal curves are smooth one dimensional curves that pass through the middle of a p dimensional data set. They minimize the distance from the points, and provide a nonlinear summary of the data. The curves are non parametric and their shape is suggested by the data. Similarly, principal surfaces are two dimensional surfaces that pass through the middle of the data. The curves and surfaces are found using an iterative procedure which starts with a liner summary such as the usual principal component line or plate. Each successive iteration is a smooth or local average of the p dimensional points, where local is based on the projections of the points onto the curve or surface of the previous iteration. A number of linear techniques, such as factor analysis and errors in variables regression, end up using the principal components as their estimates (after a suitable scaling of the coordinates). Principal curves and surfaces can be viewed as the estimates of nonlinear generalizations of these procedures. Principal Curves (or surfaces) have a theoretical definition for distributions: they are the Self Consistent curves. A curve is self consistent if each point on the curve is the conditional mean of the points that project there. The main theorem proves that principal curves are critical values of the expected squared distance between the points and the curve. Linear principal components have this property as well; in fact, we prove that if a principal curve is straight, then it is a principal component. These results generalize the usual duality between conditional expectation and distance minimization. We also examine two sources of bias in the procedures, which have the satisfactory property of partially cancelling each other
6 editions published between 1984 and 1985 in English and held by 4 WorldCat member libraries worldwide
Principal curves are smooth one dimensional curves that pass through the middle of a p dimensional data set. They minimize the distance from the points, and provide a nonlinear summary of the data. The curves are non parametric and their shape is suggested by the data. Similarly, principal surfaces are two dimensional surfaces that pass through the middle of the data. The curves and surfaces are found using an iterative procedure which starts with a liner summary such as the usual principal component line or plate. Each successive iteration is a smooth or local average of the p dimensional points, where local is based on the projections of the points onto the curve or surface of the previous iteration. A number of linear techniques, such as factor analysis and errors in variables regression, end up using the principal components as their estimates (after a suitable scaling of the coordinates). Principal curves and surfaces can be viewed as the estimates of nonlinear generalizations of these procedures. Principal Curves (or surfaces) have a theoretical definition for distributions: they are the Self Consistent curves. A curve is self consistent if each point on the curve is the conditional mean of the points that project there. The main theorem proves that principal curves are critical values of the expected squared distance between the points and the curve. Linear principal components have this property as well; in fact, we prove that if a principal curve is straight, then it is a principal component. These results generalize the usual duality between conditional expectation and distance minimization. We also examine two sources of bias in the procedures, which have the satisfactory property of partially cancelling each other
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
3 editions published in 1998 in English and held by 3 WorldCat member libraries worldwide
Classification by pairwise coupling by
Trevor Hastie(
Book
)
4 editions published between 1996 and 1997 in English and held by 3 WorldCat member libraries worldwide
4 editions published between 1996 and 1997 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
4 editions published in 1998 in English and held by 3 WorldCat member libraries worldwide
Generalized additive models, cubic splines and penalized likelihood by
Trevor Hastie(
Book
)
3 editions published in 1987 in English and held by 2 WorldCat member libraries worldwide
Generalized additive models extended the class of generalized linear models by allowing an arbitrary smooth function for any or all of the covariates. The functions are established by the local scoring procedure, using a smoother as a building block in an iterative algorithm. This paper utilizes a cubic spline smoother in the algorithm and show how the resultant procedure can be view as a method for automatically smoothing a suitably defined partial residual, and more formally, a method for maximizing a penalized likelihood. The authors also examine convergence of the inner (backfitting) loop in this case and illustrate these ideas with some binary response data. Keywords: Spline; Nonparametric regression
3 editions published in 1987 in English and held by 2 WorldCat member libraries worldwide
Generalized additive models extended the class of generalized linear models by allowing an arbitrary smooth function for any or all of the covariates. The functions are established by the local scoring procedure, using a smoother as a building block in an iterative algorithm. This paper utilizes a cubic spline smoother in the algorithm and show how the resultant procedure can be view as a method for automatically smoothing a suitably defined partial residual, and more formally, a method for maximizing a penalized likelihood. The authors also examine convergence of the inner (backfitting) loop in this case and illustrate these ideas with some binary response data. Keywords: Spline; Nonparametric regression
Principal component models for sparse functional data by Gareth James(
Book
)
3 editions published in 1999 in English and held by 2 WorldCat member libraries worldwide
3 editions published in 1999 in English and held by 2 WorldCat member libraries worldwide
Degrees of freedom tests for smoothing splines by
Eva Cantoni(
Book
)
3 editions published in 2000 in English and held by 2 WorldCat member libraries worldwide
3 editions published in 2000 in English and held by 2 WorldCat member libraries worldwide
Estimating the number of clusters in a dataset via the gap statistic by
Robert Tibshirani(
Book
)
3 editions published in 2000 in English and held by 2 WorldCat member libraries worldwide
3 editions published in 2000 in English and held by 2 WorldCat member libraries worldwide
Statistical models for image sequences by
Neil Crellin(
Book
)
3 editions published in 1999 in English and held by 2 WorldCat member libraries worldwide
3 editions published in 1999 in English and held by 2 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
4 editions published in 1994 in English and held by 2 WorldCat member libraries worldwide
Dynamic mixtures of splines: a model for saliency grouping in the time frequency plane by
Stanford University(
Book
)
3 editions published in 1997 in English and held by 2 WorldCat member libraries worldwide
3 editions published in 1997 in English and held by 2 WorldCat member libraries worldwide
Conditional random sampling : a sketchbased sampling technique for sparse data by Ping Li(
Book
)
2 editions published in 2006 in English and held by 1 WorldCat member library worldwide
2 editions published in 2006 in English and held by 1 WorldCat member library worldwide
more
fewer
Audience Level
0 

1  
Kids  General  Special 
Related Identities
 Tibshirani, Robert Other Thesis advisor Author Contributor
 Friedman, J. H. (Jerome H.) Author
 Witten, Daniela Other
 Chambers, John M. 1941 Author Editor
 James, Gareth (Gareth Michael) Author
 Friedman, Jerome Author
 Efron, Bradley Thesis advisor Author
 Wainwright, Martin (Martin J.) Author Contributor
 Wainwright, Martin
 James, Gareth Author
Useful Links
Associated Subjects
Artificial intelligence Bayesian statistical decision theory Bioinformatics BiologyData processing Computational biology Computational intelligence Computer science Database management Data mining Electronic data processing Estimation theory Forecasting Inference Least squares Linear models (Statistics) Linear models (Statistics)Computer programs Machine learning Machine learningStatistical methods Mathematical models Mathematical statistics Mathematical statisticsData processing MathematicsData processing Multivariate analysis Paired comparisons (Statistics) Principal components analysis Probabilities Proof theory R (Computer program language) Random walks (Mathematics) Regression analysis S (Computer program language) Smoothing (Statistics) Sparse matrices Statistics StatisticsGraphic methods StatisticsMethodology Supervised learning (Machine learning)
Alternative Names
Hastie, T. J.
Hastie, T. J. 1953
Hastie, T. J. (Trevor J.)
Hastie, T. J. (Trevor J.), 1953
Hastie, Trevor.
Hastie, Trevor 1953...
Hastie, Trevor J.
Hastie, Trevor J. 1953
Trevor Hastie Amerikaans statisticus
Trevor Hastie statisticien
Trevor Hastie statistico statunitense
Trevor Hastie statistiker
Trevor J. Hastie
Тревор Хасти
ヘイスティ, T. J
Languages
Covers