Trevor Hastie

Dados Biográficos

ID1809223
NOMETrevor Hastie
PRENOMESTrevor
SOBRENOMEHastie
ASSINATURAHASTIE T
AFILIAÇÕESStanford University
ORCID0000-0002-0164-3142
VERIFICADOSim
TOTAL DE OBRAS13
TOTAL DE CITAÇÕES0
TOTAL COMO AUTOR13
TOTAL COMO EDITOR0
PRIMEIRO ANO DE PUBLICAÇÃO1986
ANO MAIS RECENTE DE PUBLICAÇÃO2023
ÍNDICE H0

  • Reorienting Latent Variable Modeling for Supervised Learning

    Open AccessBooil Jo, Trevor Hastie et al.ARTICLEMultivariate Behavioral Research2023

    Despite its potentials benefits, using prediction targets generated based on latent variable (LV) modeling is not a common practice in supervised learning, a dominating framework for developing prediction models. In supervised learning, it is typically assumed that the outcome to be predicted is clear and readily available, and therefore validating outcomes before predicting them is a foreign concept and an unnecessary step. The usual goal of LV …

  • Principal component analysis

    Open AccessM Greenacre, Patrick J F Groenen et al.ARTICLENature Reviews Methods Primers2022

  • Causal Interpretations of Black-Box Models

    QINGYUAN ZHAO, Trevor HastieARTICLEJournal of Business and Economic…2021

    The fields of machine learning and causal inference have developed many concepts, tools, and theory that are potentially useful for each other. Through exploring the possibility of extracting causal interpretations from black-box machine-trained models, we briefly review the languages and concepts in causal inference that may be interesting to machine learning researchers. We start with the curious observation that Friedman's partial dependence p…

  • A statistical explanation of MaxEnt for ecologists

    Open AccessJane Elith, Steven J Phillips et al.ARTICLEDiversity and Distributions2011

    MaxEnt is a program for modelling species distributions from presence-only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence-only data, …

  • 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…

  • A working guide to boosted regression trees

    Open AccessJane Elith, John R Leathwick et al.ARTICLEJournal of Animal Ecology2008

    1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression tre…

  • Regularization and Variable Selection Via the Elastic Net

    Open AccessHui Zou, Trevor HastieARTICLEJournal of the Royal Statistical…2005

    We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together. The elastic net is particularly useful when the number of predictors (p) is much bi…

  • 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 …

  • 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 …