WorldCat Identities

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 full-color 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
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 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 )

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, 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
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

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 twenty-first 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 High-Dimensional 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 up-to-date 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

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

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 non-linear 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 co-ordinates). Principal curves and surfaces can be viewed as the estimates of non-linear 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

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

Bayesian backfitting by Trevor Hastie( Book )

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; Non-parametric 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

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

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

Statistical models for image sequences by Neil Crellin( Book )

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

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

Conditional random sampling : a sketch-based sampling technique for sparse data by Ping Li( Book )

2 editions published in 2006 in English and held by 1 WorldCat member library worldwide

 
moreShow More Titles
fewerShow Fewer Titles
Audience Level
0
Audience Level
1
  Kids General Special  
Audience level: 0.65 (from 0.58 for An introdu ... to 0.86 for Tōkeiteki ...)

WorldCat IdentitiesRelated Identities
The elements of statistical learning : data mining, inference, and prediction : with 200 full-color illustrations
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
English (251)

Japanese (5)

Dutch (1)

Covers
Generalized additive modelsStatistical models in S