PAPERS  

  2009

• Rahul Mazumder, Jerome Friedman and Trevor Hastie: SparseNet: Coordinate Descent with Non-Convex Penalties. Non-convex penalties produce sparser models than the LASSO, but pose difficulties for optimization. We propose a structured algorithm using coordinate descent which finds good solutions with guaranteed convergence properties.
• Rahul Mazumder, Trevor Hastie and Rob Tibshirani: Spectral Regularization Algorithms for Learning Large Incomplete Matrices. (submitted) We develop an iterative algorithm for matrix completion using nuclear-norm regularization.
• Daniela Witten, Rob Tibshirani and Trevor Hastie: A penalized matrix decomposition, with applications to sparse canonical correlation analysis and principal components Biostatistics (epub)



  2008

• Tong Tong Wu, Yi Fang Chen, Trevor Hastie, Eric Sobel and Kenneth Lange: Genomewide Association Analysis by Lasso Penalized Logistic Regression We develop efficient computational procedures for screening large-scale genome-wide association studies. Bioinformatics 25(6): 714-721, 2009.
• Ping Li, Ken Church and Trevor Hastie: One sketch for all: theory and application of conditional random sampling to appear, Nips08 proceedings.
• Jerome Friedman, Trevor Hastie and Robert Tibshirani: Regularized Paths for Generalized Linear Models via Coordinate Descent. We use coordinate descent to develop regularization paths for linear, logistic and multinomial regression models. Our algorithms use the "elastic net" penalties of Zou and Hastie (2005), and create the path for a grid of values of the penalty parameter lambda. Journal of Statistical Software, 33(1), 2010
  The R package glmnet is available from CRAN
  A matlab wrapper for the glmnet fortran code, written by Hui Jiang.
• Jane Elith, John Leathwick and Trevor Hastie: A working guide to boosted regression trees (2008) Journal of Animal Ecology, 77 802-813. Here are the online supplement materials, along with the associated zip file.
• Line Clemmensen, Trevor Hastie and Bjarne Ersboll: Sparse Discriminant Analysis. We extend penalized linear and mixture discriminant analysis by incorporating a lasso penalty to encourage sparseness.
• John Leathwick, Jane Elith, W. Chadderton, D. Rowe and Trevor Hastie: Dispersal, disturbance and the contrasting biogeophraphies of New Zealand's diadromous and non-diadronous fish species. An application of boosted regression trees in ecological mapping. J. Biogeography 2008, 35 1481-1497.
• Debashis Paul, Eric Bair, Trevor Hastie and Robert Tibshirani: "Preconditioning" for feature selection and regression in high-dimensional problems We show that supervised principal components followed by a variable selection procedure is an effective approach for variable selection in very high dimension. Annals of Statistics 36(4), 2008, 1595-1618.



  2007

• Jerome Friedman, Trevor Hastie and Robert Tibshirani, Sparse inverse covariance estimation with the lasso. We develop an effecient algorithm for solving the L1-penalized likelihood approach to sparse covariance estimation.
• Trevor Hastie Comment on a paper in Statistical Science by Peter Bühlmann and Torsten Hothorn: Boosting Algorithms:Regularization, Prediction and Model Fitting (2007) 22(4),477-522.
• Jerome Friedman, Trevor Hastie and Robert Tibshirani, Discussion of "Evidence contrary to the statistical view of boosting (David Mease and Aaron Wyner)" Wyner and Mease show through examples some counter-intuitive results with boosting, that appear to contradict our 2000 paper. We discount these claims by reversing their results using shrinkage along with boosting. JMLR9 (2008) 59-64.
• Jerome Friedman, Trevor Hastie, Holger Hoefling and Robert Tibshirani, Pathwise Coordinate Optimization. We show how coordinate descent algorithms can efficiently solve a number of popular regularized optimization problems, creating an entire path of solutions. We generalize this approach to derive an efficient algorithm for the fused lasso, both one- and two-dimensional. Annals of Applied Statistics (2007), 1(2), 302-332.
• Ping Li, Trevor Hastie and Kenneth Church. Nonlinear Estimators and Tail Bounds for Dimension Reduction in L1 using Cauchy Random Projections. We provide improved methods for approximating L1 distances in very high dimensions, based on maximum-likelihood estimation in the Cauchy family. JLMR 8, pp 2497-2532
• Ping Li and Trevor Hastie. A Unified Near-Optimal Estimator for Dimension Reduction in L_a (0< a <= 2) Using Stable Random Projections. NIPS2007 poster presentation
• Brad Efron, Trevor Hastie and Rob Tibshirani, Discussion of the "Dantzig Selector" by Emmanuel Candes and Terrence Tao. Candes and Tao propose an alternative but similar procedure to the lasso. This discussion appears alongside the orginal article in the Annals of Statistics 35(6), pp2358-2364.
• Trevor Hastie, Jonathan Taylor, Robert Tibshirani and Guenther Walther, Forward Stagewise Regression and the Monotone Lasso We characterize the incremental forward stagewise procedure as a monotone version of the lasso. Electronic Journal of Statistics 1 (2007).



  2006

• Trevor Hastie and Ji Zhu, Discussion of "Support Vector Machines with Applications" by Javier M. Moguerza and Alberto Munoz, Statistical Science 21(3) 352-357.
• Gill Ward, Trevor Hastie, Simon Barry, Jane Elith and John Leathwick, Presence-only data and the EM algorithm. We develop a method for fitting the two-class logistic regression model using labeled data from one class, a sample of unlabeled data, and knowledge of the class prevalences.
  A presentation by Gill Ward based on this work "Making the Best Use of Available Data: The Presence-Only Problem in Ecology" won an honorable mention award at the 2007 Joint Statistical Meetings.
• John Leathwick, Jane Elith and Trevor Hastie, Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. (2006) Ecological Modelling 199 188-196. This is a special issue of the journal devoted to the workshop on Advances in Predictive Species Distribution Models held in Riederalp,Switzerland, 2004.
• Ping Li, Trevor Hastie and Kenneth Church, Very Sparse Random Projections. A method for approximating pairwise distances in very high-dimensional spaces. Best student paper, KDD-06, Philadelphia
• Mee-Young Park and Trevor Hastie, Regularization Path Algorithms for Detecting Gene Interactions. We develop a path algorithm for fitting the "cosso" models of Yuan & Lin (2006) with logistic regression. This allows factors and interactions to enter the model in a smooth way.
• Mee-Young Park and Trevor Hastie, Penalized Logistic Regression for Detecting Gene Interactions. A modified version of forward-stepwise logistic regression suitable for screening large numbers of gene-gene interactions.
  stepPlr: R package for fitting PLR models.
• Mee-Young Park, Trevor Hastie and Rob Tibshirani, Averaged gene expressions for regression A regression method that combines the lasso with hierarchical clustering, intended for selecting groups of genes in microarray problems. Biostatistics (in press; epub). R Software for fitting these models.
• Yaqian Guo, Trevor Hastie and Robert Tibshirani Regularized Discriminant Analysis and its Application in Microarrays. A method, similar to shrunken centroids, for classification and discrimination of microarrays, using regularized discriminant analysis with gene selection. Biostatistics (in press; epub)
• Mee-Young Park and Trevor Hastie, An L1 Regularization-path Algorithm for Generalized Linear Models. A generalization of the LARS algorithm for GLMs and the Cox proportional hazard model. Since the coefficient paths are piecewise-nonlinear, approximations are made using the predictor-corrector algorithm of convext optimization. glmpath: R software package for fitting L1 regularized GLMs and Cox models. (JRSSB 2007 (69, part 4), pages 659-677 )
• Ping Li, Trevor Hastie and Kenneth Church, Improving Random Projections Using Marginal Information. Methods for speeding up document search and characterization. Accepted at Colt 2006 This paper draws on results in the following two technical reports:
  Ping Li, Trevor Hastie, and Kenneth Church, Margin-constrained Random Projections and Very Sparse Random Projections.
  Ping Li, Kenneth Church and Trevor Hastie, A Sketch-based Sampling Algorithm on Sparse Data
• Rob Tibshirani and Trevor Hastie, Margin Trees for High-dimensional Classification. A tree-structured representation for a multiclass SVM classifier.
• Hui Zou, Ji Zhu and Trevor Hastie, New Multicategory Boosting Algorithms Based on Multicategory Fisher-Consistent Losses. We provide some general requirements for multiclass margin-based classifiers. Annals of Applied Statistics 2(4) pp 1290-1306, 2008).



  2005

• Ji Zhu, Hui Zhou, Saharon Rosset and Trevor Hastie, Multi-class Adaboost. A multi-class generalization of the Adaboost algorithm, based on a generalization of the exponential loss.
  Finally published in 2009 in Statistics and Its Interface Volume 2 (2009) 349-360.
• J. Leathwick, J. Elith, M. Francis, T. Hastie, P. Taylor. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees.
  (Marine Ecology Progress Series, published in 2006). A detailed analysis of species abundance using Poisson regression with boosted regression trees. All analysis done using the gbm package in R (Greg Ridgeway).
• J. Leathwick, D. Rowe, J. Richardson, J. Elith and T. Hastie, Using multivariate adaptive regression splines to predict the distributions of New Zealand's freshwater diadromous fish. Freshwater Biology 50 2034-2051.
  Presence-absence species data are modelled using a MARS along with GLM in R.
• Mee-Young Park and Trevor Hastie, Hierarchical Classification using Shrunken Centroids. A technique for classification when the number of classes is large. It produces an hierarchically structured classification rule, with the hardest-to-separate classes at the terminal nodes.

  2004

• Hui Zou, Trevor Hastie, and Rob Tibshirani, On the "Degrees of Freedom" of the Lasso. A technical paper that establishes that the number of non-zero coefficients in a lasso model is unbiassed for the effective degrees of freedom. Published in Annals of Statistics (2007), 35, 5, 2173-2192.
• Philip Beineke, Trevor Hastie and Shivakumar Vaithyanathan, The Sentimental Factor: Improving Review Classification via Human-Provided Information Proceedings ACL 2004, Barcelona. (ACL: Association of Computational Linguistics)
• Eric Bair, Trevor Hastie, Debashis Paul, and Robert Tibshirani Prediction by Supervised Principal Components Published in JASA 2006 101 No 473, pp 11-137.
• NIPS2004 - The following papers were accepted for NIPS 2004:
  Trevor Hastie, Saharon Rosset, Rob Tibshirani and Ji Zhu, The Entire Regularization Path for the Support Vector Machine (oral presentation)
  Saharon Rosset, Hui Zou, Ji Zhu and Trevor Hastie, A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning (poster presentation)
  
• Hui Zou, Trevor Hastie, and Rob Tibshirani. Sparse Principal Component Analysis. We present a new approach to principal component analysis, that allows us to use an L1 penalty to ensure sparseness of the loadings. Published in JCGS 2006 15(2): 262-286. Software is available in R package elasticnet available from CRAN.
• Trevor Hastie, Saharon Rosset, Rob Tibshirani and Ji Zhu. The Entire Regularization Path for the Support Vector Machine. JMLR, 5(Oct) 1391-1415. An algorithm for computing the two-class SVM solution for all possible values of the regularization parameter C, at essentially the computational cost of a single SVM fit. Not only does this allow for efficient model selection, but it also exposes the role of regularization for SVMs. Several MPEG movies show the sequence of solutions for different examples. SvmPath software package for R.
• Trevor Hastie and Robert Tibshirani. Efficient Quadratic Regularization for Expression Arrays. Biostatistics (2004), 5(3), pp 329-340. Computational tricks for a large class of linear models fit by quadratic regularization.

  2003

• Hui Zou and Trevor Hastie. Regularization and Variable Selection via the Elastic Net (pdf). JRSSB (2005) 67(2) 301-320. A compromise between ridge regression and the lasso, with the computational advantages of the lasso. The elastic net selects variables in correlated sets. An R package elasticnet is available from CRAN.
  See interview on Essential Science Indicators.
  Published minor correction.
• Jerome Friedman, Trevor Hastie, Saharon Rosset, Rob Tibshirani and Ji Zhu. Discussion of three Boosting papers Annals of Statistics, 2004, vol 32 (1) pp 102-107. The three papers are by (1) Wenxin Jiang, (2) Gabor Lugosi and Nicolas Vayatis, and (3) Tong Zhang.
• Saharon Rosset, Ji Zhu and Trevor Hastie. Margin Maximizing Loss Functions (accepted poster Nips 2003)
• Ji Zhu, Saharon Rosset, Trevor Hastie and Rob Tibshirani. 1-Norm Support Vector Machines (accepted spotlight poster Nips 2003)
• Mu Zhu, Trevor Hastie and Guenther Walther. Constrained Ordination Analysis with Flexible Response Functions Constrained ordination via nonparametric discriminant analysis. Ecological Modelling (2005), 187(4), 524--536.
• Francesca Dominici, Aidan McDermott and Trevor Hastie. Improved Semiparametric Time Series Models of Air Pollution and Mortality [pdf Technical Report] JASA, December 2004, 99(468), 938-948. [Sofware]
• Ji Zhu and Trevor Hastie Classification of Gene Microarrays by Penalized Logistic Regression. Biostatistics 5(3):427-443. S Code (from Ji Zhu's website)

  2002

• Trevor Hastie and Robert Tibshirani. Independent Component Analysis through Product Density Estimation (ps file). A direct statistical approach to ICA, using an attractive spline representation to model each of the marginal densities.A more recent (Nov 2002) talk (pdf)
• Saharon Rosset, Ji Zhu and Trevor Hastie. Boosting as a Regularized Path to a Maximum Margin Classifier (pdf file) JMLR 5 (Aug 2004): 941--973, 2004. We show that a version of boosting fits a model by optimizing a L1-penalized loss function. This in turn shows that the corresponding versions of Adaboost and Logitboost converge to an "L1" optimal separating hyperplane.
• Support Vector Machines, Kernel Logistic Regression and Boosting . Slides for talk given at Spring research conference in Michigan, MCS2002 in Sardinia, NPCONF2002 in Crete, and ASA2002 in New York.
• Hongjuan Zhao, Trevor Hastie, Dr Michael L Whitfield, Prof Anne-Lise Borresen-Dale and Dr. Stefanie S Jeffrey
  Optimization and evaluation of T7 based RNA linear amplification protocols for cDNA microarray analysis
  BMC Genomics 2002, 3:31 (30 Oct 2002) Biomed central online
  
  
• Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, and Gilbert Chu. Class prediction by nearest shrunken centroids, with applications to DNA microarrays (ps file) (pdf file) This is a more statistical version of the PNAS paper below.
• Rob Tibshirani, Trevor Hastie, B. Narashiman and Gilbert Chu: "Diagnosis of multiple cancer types by shrunken centroids of gene expression" (PNAS website). PNAS 2002 99:6567-6572 (May 14). See PAM website for software (available soon).
• Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani, Least Angle Regression Annals of Statistics (with discussion) (2004) 32(2), 407-499. A new method for variable subset selection, with the lasso and "epsilon" forward stagewise methods as special cases. LARS Software for R and Splus.
• Antoine Guisan, Thomas Edwards and Trevor Hastie Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modeling (2002) 157, 89-100.

  2001

• Trevor Hastie, Robert Tibshirani and Jerome Friedman, "Elements of Statistical Learning: Data Mining, Inference and Prediction" Springer-Verlag, New York.
• Mu Zhu and Trevor Hastie, "Feature extraction for non-parametric discriminant analysis" JCGS (2003, 12(1), pages 101-120.
• Ji Zhu and Trevor Hastie, "Kernel Logistic Regression and the Import Vector Machine", (NIPS, 2001; JCGS 2005). Copy of slides(pdf) presented by TH in Kyoto in December, 2001.
• Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, Michael Eisen, Gavin Sherlock, Pat Brown, and David Botstein Exploratory screening of genes and clusters from microarray experiments (ps file) or pdf version.
• Therese Sorlie, Perou, C., Robert Tibshirani, Turid Aas, Stephanie Geisler, Hilde Johnsen, Trevor Hastie, Michael B. Eisen, Matt van de Rijn, Stefanie S. Jeffrey, Thor Thorsen, Hanne Quist, John C. Matese, Patrick O. Brown, David Botstein, Per Eystein Lonninngg, and Anne-Lise Borresen-Dale. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. PNAS 98: 10869-10874. pdf version.

  2000

• Trevor Hastie, Robert Tibshirani, David Botstein and Pat Brown, "Supervised Harvesting of Expression Trees" (postscript) . Starting from a hierarchically clustered expression array, we build a predictive model for an outcome variable using cluster nodes as inputs.
  (pdf version) Tech. report. August 2000.
• Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, Missing value estimation methods for DNA microarrays BIOINFORMATICS Vol. 17 no. 6, 2001 Pages 520-525
• Eva Cantoni and Trevor Hastie "Degrees-of-Freedom Tests for Smoothing Splines." Tech Report, May 2000.
  Published in Biometrika 2002, 89, 251-263. A mixed-effects framework for smoothing splines and additive models allows for exact tests between nested models of different complexity. The complexity is calibrated via the effective degrees of freedom.
• Thomas Yee and Trevor Hastie. Reduced Rank Vector Generalized Linear Models (2003) Statistical Modeling, 3, pages 15-41. Using the multinomial as a primary example, we propose reduced rank logit models for discrimination and classification. This is a conditional version of the reduced rank model of linear discriminant analysis.
• Robert Tibshirani, Guenther Walther and Trevor Hastie.
  "Estimating the number of clusters in a dataset via the Gap statistic". Journal of the Royal Statistical Society, B, 63:411-423,2001.
• Stochastic Modeling and Tracking of Human Motion, a joint project with Dirk Ormoneit and Michael Black's group at Xerox Parc, with motion graphics demonstrations of learned walking characteristics.
• Page 50 of "Generalized Additive Models" by Hastie and Tibshirani, 1990, Chapman and Hall. Some copies of the 1999 printing by CRC Press replaced page 50 with a page from a history text! page50.ps or page50.pdf
• Trevor Hastie, Laura Bachrach, Balasubramanian Narasimhan and May Choo Wang. Flexible Statistical Models for Growth Fragments: a Study of Bone Mineral Acquisition Compare your own measurements using our online growth tables
• Gareth James and Trevor Hastie Functional Linear Discriminant Analysis for Irregularly Sampled Curves (2001) Journal of the Royal Statistical Society, Series B JRSS B 63, 533-550.
• Trevor Hastie, Robert Tibshirani, Michael B Eisen, Ash Alizadeh, Ronald Levy, Louis Staudt, Wing C Chan, David Botstein, Patrick Brown. `Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns This is an online version of the paper, published in the online journal GenomeBiology.
• Trevor Hastie, Robert Tibshirani, Michael Eisen, Pat Brown, Doug Ross, Uwe Scherf, John Weinstein, Ash Alizadeh, Louis Staudt, David Botstein "Gene Shaving: a New Class of Clustering Methods for Expression Arrays". Postscript (2.9mb) or Adobe pdf (5.4mb) Tech. report. Jan 2000.
• James, G., Hastie, T., and Sugar, C. "A Principal Component Models for Sparse Functional Data". (2000, Biometrika, 87, 587-602) (pdf). When the data are collections of sampled curves or images, functional principal components produce the principal modes of variation. Here we generalize these procedures to deal with the case when each curve is sparsely and irregularly sampled.

  1999

• Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M., Brown, P. and Botstein, D. "Imputing Missing Data for Gene Expression Arrays". Technical report (1999), Stanford Statistics Department. pdf (145Kb) or postscript (450Kb)
• Tibshirani, R., Hastie, T. Eisen, M., Ross, D. , Botstein, D. and Brown, P. "Clustering methods for the analysis of DNA microarray data". Postscript (4.8mb) or Compressed Postscript (1.8mb) Tech. report Oct. 1999.
• D. Ormoneit and T. Hastie. Optimal kernel shapes for local linear regression. In S. A. Solla, T. K. Leen, and K-R. Müller, editors, Advances in Neural Information Processing Systems 12. The MIT Press, 2000. To appear.
• Tibshirani, R. and Lazzeroni, L. and Hastie, T. and Olshen, A. and Cox, D.R. "A Global Pairwise Approach to Radiation Hydrid Mapping". Technical Report January 1999. Using data of co-occurrence of hybridized markers after shattering, inference is made of the marker sequence in the chromosome.

  1998

• Friedman, J., Hastie, T. and Tibshirani, R. (Published version) Additive Logistic Regression: a Statistical View of Boosting Annals of Statistics 28(2), 337-407. (with discussion)
  We show that boosting fits an additive logistic regression model by stagewise optimization of a criterion very similar to the log-likelihood, and present likelihood based alternatives. We also propose a multi-logit boosting procedure which appears to have advantages over other methods proposed so far. Here are the slides (2 per page) for my boosting talk.
• Crellin, N., Hastie, T. and Johnstone, I. "Statistical Models for Image Sequences" Technical report, submitted to "Human Brain Mapping". We study fMRI sequences of the human brain obtained from experiments involving repetitive neuronal activity. We investigate the function form of the hemodynamic response function, and provide evidence that the commonly adopted convolution model is inadequate.
• Hastie, T. and Tibshirani, R. "Bayesian Backfitting" Stanford Technical report.
  The Gibbs sampler looks and feels like the backfitting algorithm for fitting additive models. Indeed, a simple modification to backfitting turns it into a Gibbs sampler for spitting out samples from the "posterior" distribution for an additive fit.
  Published Statistical Science 15, no. 3 (2000), 196-223
• Wu, T.,Hastie, T., Schmidler, S. and Brutlag, D. "Regression Analysis of Multiple Protein Structures" Models for lining up and averaging groups of protein structures.

  1997

• Rubinstein, D. and Hastie, T. "Discriminative vs Informative Learning" A comparison of two frequently used but different paradigms for training classifiers.
• Maes, S. and Hastie, T. "Dynamic Mixtures of Splines: a Model for Saliency Grouping in the Time Frequency Plane" This is an application of mixture modeling to speech data. We use a moving mixture of Gaussians to represent the formant-frequencies in speech data.
• Hastie, T., and Tibshirani, R. and Buja, A. "Flexible Discriminant and Mixture Models" To appear in edited proceedings of "Neural Networks and Statistics" conference, Edinburgh, 1995. J. Kay and D. Titterington, Eds. Oxford University Press.
• Wu, T., Schmidler, S., Hastie, T., and Brutlag, D. "Modelling and superposition of multiple protein structures using affine transformations: analysis of the globins"
• James, G., and Hastie, T. "Generalizations of the Bias/Variance Decomposition for Prediction Error". Several papers have recently appeared on this topic, and each have a different viewpoint and decomposition. We hope ours does not add to the confusion.
• James, G., and Hastie, T. Error Coding and PaCTs. Gareth James' winning paper in the ASA student paper competition for the Statistical Computing Section. This is one of four winning papers.

  1996

• Hastie, T. "Neural Networks", to appear in Encyclopaedia of Biostatistics. A brief survey with some personal points of view.
• Hastie, T., and Tibshirani, R. "Classification by Pairwise Coupling" We solve a multiclass classification problem by combining all the pairwise rules. This paper builds on ideas proposed by J. Friedman. An abbreviated version is published in Advances in Neural Information Processing Systems 10, M. I. Jordan, M. J. Kearns, S. A. Solla, eds., MIT Press, 1998.
• Hastie, T. and Tibshirani, R. "Generalized Additive Models" to appear in "Encyclopaedia of Statistical Sciences". A survey paper on GAMs.

  1995

• Hastie, T. and Simard, P. "Models and Metrics for Handwritten Character Recognition". This paper gives a brief survey of techniques for handwritten digit recognition, and then goes into some particular technique based on invariant distance in some detail (0.5Mb compressed postscript)
• Hastie, T. and Tibshirani, R. "Discriminant Adaptive Nearest Neighbor Classification." IEEE PAMI, 18, 607-616, 1996.
• Hastie, T. and Tibshirani, R. "Generalized Additive Models for Medical Research" to appear in "Encyclopaedia for Biostatistics"

  1994

• Hastie, T. and Tibshirani, R. "Discriminant Analysis by Gaussian Mixtures." JRSSB (Jan 1996). There is also a longer technical report of Feb 1994
• Hastie, T. J., Buja, A., and Tibshirani, R. "Penalized Discriminant Analysis." (Bell Labs technical report; postscript). Pdf of published version, Annals of Statistics, 1995.
• Hastie, T. J., Simard, P. Y., and Saeckinger, E. "Learning Prototype Models for Tangent Distance." NIPS proceedings, 1994.
• Hastie, T.J and Tibshirani, R. "Handwritten Digit Recognition via Deformable Prototypes." AT&T Bell Laboratories Technical Report, 1994.

  1993

• Hastie, T. J., Tibshirani, R. and Buja, A. "Flexible Discriminant Analysis by Optimal Scoring." (Bell Labs Technical report; postscript). Pdf of published version, JASA, December 1994.
• Roosen, C. B. and Hastie, T. "Automatic Smoothing Spline Projection Pursuit." AT&T Bell Labs Technical Report (Dec. 1993).
• Roosen, C. B. and Hastie, T. "Logistic Response Projection Pursuit." AT&T Bell Labs Technical Report (Aug. 1993).

  1992

• Hastie, T. J., Kishon, E., Clark, M., and Fan, J. "A Model for Signature Verification." AT&T Bell Laboratories Technical Report (Feb. 1992).

  1990

• Hastie, T. J., and Pregibon, D. "Shrinking Trees." AT&T Bell Laboratories Technical Report (March 1990). Unpublished manuscript. Thanks to Mu Zhu for turning the pre-web technical memorandum into an online document.

  1989

• Buja, A. Hastie, T. J., Tibshirani, R. Linear Smoothers and Additive Models An overview of linear smoothing technology, including a proof of the convergence of the backfitting algorithm. Annals of Statistics (with discussion) 1989, 17(2) 453-555.
• Hastie, T., Botha, J., Schnitzler, C. Regression with an Ordered Categorical Response Statistics in Medicine 1989, 8 785-794

  1987

• Hastie, T. J., and Pregibon, D. "A new algorithm for matched case-control studies with applications to additive models." AT&T Bell Laboratories Technical Report (November 1987). Unpublished manuscript. A shorter version appeared in the proceedings of Compstat 1988
• Hastie, T. and Little, F. Principal Profiles A nonlinear version of principal components for compositional data, that helps explain the horse-shoe effect. Published in Proceedings of the Interface Meeting (Comp.Sci and Stat), 1987.

  1986

• Hastie, Trevor, and Tibshirani, R. "Generalized Additive Models (with discussion)" Statistical Science Vol 1, No 3, pages 297-318

  1984

• Principal Curves and Surfaces SLAC (Stanford Linear Accelerator Center) has put up a pdf version of my Ph.D thesis.

  1980

• The Stability of Ordinal Measures of Association in Contingency Tables My University of Cape Town MS thesis.