T. Hastie

T. Hastie, Michael Lim, 2015, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

Margin Maximizing Loss Functions

Ji Zhu, Trevor J. Hastie, Saharon Rosset, 2003, NIPS.

Very sparse random projections

Kenneth Ward Church, Trevor J. Hastie, Ping Li, 2006, KDD '06.

Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival.

Howard Y. Chang, Yudong D. He, R. Tibshirani, 2005, Proceedings of the National Academy of Sciences of the United States of America.

Predicting multivariate responses in multiple linear regression - Discussion

Trevor Hastie, David J. Hand, Robert Tibshirani, 1997 .

Presence‐Only Data and the EM Algorithm

T. Hastie, J. Elith, J. Leathwick, 2009, Biometrics.

Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort

T. Hastie, E. Ashley, M. Wheeler, 2016, bioRxiv.

Bayesian backfitting (with comments and a rejoinder by the authors

R. Tibshirani, T. Hastie, 2000 .

Margin Trees for High-dimensional Classification

Robert Tibshirani, Trevor J. Hastie, R. Tibshirani, 2007, J. Mach. Learn. Res..

Hierarchical Classification using Shrunken Centroids

Mee Young Park, T. Hastie, 2005 .

Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso

R. Tibshirani, T. Hastie, R. Tibshirani, 2017, 1707.08692.

The Sentimental Factor: Improving Review Classification Via Human-Provided Information

Trevor J. Hastie, Shivakumar Vaithyanathan, Philip Beineke, 2004, ACL.

Integration of Mechanistic Immunological Knowledge into a Machine Learning Pipeline Increases Predictive Power

Trevor Hastie, Robert Tibshirani, Martin Becker, 2020, bioRxiv.

Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees

T. Hastie, J. Elith, J. Leathwick, 2006 .

Using multivariate adaptive regression splines to predict the distributions of New Zealand ’ s freshwater diadromous fish

T. Hastie, J. Elith, J. Leathwick, 2005 .

Estimating the number of clusters in a data set via the gap statistic

Robert Tibshirani, R. Tibshirani, T. Hastie, 2000 .

Monographs on statistics and applied probability

R. Tibshirani, T. Hastie, V. Napolioni, 1990 .

Improved Semiparametric Time Series Models of Air Pollution and Mortality

T. Hastie, F. Dominici, A. McDermott, 2004 .

A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank

Trevor Hastie, Robert Tibshirani, Yosuke Tanigawa, 2019, bioRxiv.

Cell type–specific gene expression differences in complex tissues

Mark M. Davis, R. Tibshirani, T. Hastie, 2010, Nature Methods.

3-D curve matching using splines

Trevor J. Hastie, Haim J. Wolfson, Eyal Kishon, 1990, J. Field Robotics.

A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning

Ji Zhu, Hui Zou, Trevor J. Hastie, 2004, NIPS.

CUSTOMIZED TRAINING WITH AN APPLICATION TO MASS SPECTROMETRIC IMAGING OF CANCER TISSUE.

Trevor Hastie, Robert Tibshirani, Scott Powers, 2015, The annals of applied statistics.

Data Representation and Compression Using Linear-Programming Approximations pdf

John C. Mitchell, Trevor J. Hastie, Hristo S. Paskov, 2015, ICLR.

Learning Mixed Graphical Models pdf

Trevor J. Hastie, Jason D. Lee, T. Hastie, 2012, ArXiv.

The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression

Sarah E. Chang, Brooke R. Staveland, T. Hastie, 2020, NeuroImage.

Classification by Pairwise Coupling

Robert Tibshirani, Trevor J. Hastie, R. Tibshirani, 1997, NIPS.

Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets

Joel Dudley, Rong Chen, Atul J. Butte, 2010, PLoS Comput. Biol..

Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data

Kenneth Ward Church, Trevor J. Hastie, Ping Li, 2006, NIPS.

Sample classification from protein mass spectrometry, by 'peak probability contrasts'

Robert Tibshirani, Trevor J. Hastie, Balasubramanian Narasimhan, 2004, Bioinform..

Transparency and reproducibility in artificial intelligence

Trevor Hastie, Joelle Pineau, Robert Tibshirani, 2020, Nature.

Undirected Graphical Models

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

An Efficient Algorithm for Large Scale Compressive Feature Learning

John C. Mitchell, Trevor J. Hastie, Hristo S. Paskov, 2014, AISTATS.

The elements of statistical learning. 2001

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2001 .

Evaluating quantitative proton‐density‐mapping methods

Trevor Hastie, Ariel Rokem, Shai Berman, 2016, Human brain mapping.

Quantitative measurements of alternating finger tapping in Parkinson's disease correlate with UPDRS motor disability and reveal the improvement in fine motor control from medication and deep brain stimulation

T. Hastie, G. Jefferis, G. Heit, 2005, Movement disorders : official journal of the Movement Disorder Society.

Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data.

T. Hastie, K. Ramchandran, D. Rubin, 2018, Journal of the National Cancer Institute.

ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system

Trevor Hastie, Atul J Butte, Jacob J Hughey, 2016, Nucleic acids research.

PATHWISE COORDINATE OPTIMIZATION

R. Tibshirani, T. Hastie, J. Friedman, 2007, 0708.1485.

Modeling and superposition of multiple protein structures using affine transformations: analysis of the globins.

D L Brutlag, T Hastie, T. Hastie, 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

Automatic Bias Correction Methods in Semi-supervised Learning

H. Zou, T. Hastie, Ji Zhu, 2019 .

Stable random projections and conditional random sampling, two sampling techniques for modern massive datasets

T. Hastie, Ping Li, 2007 .

Practical Procedures for Dimension Reduction in l 1

Kenneth Ward Church, T. Hastie, Ping Li, 2022 .

Linear Model Selection and Regularization

Trevor Hastie, Robert Tibshirani, Gareth M. James, 2021, Springer Texts in Statistics.

Point process models for presence‐only analysis

Steven J. Phillips, T. Hastie, William Fithian, 2015 .

Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions

T. Hastie, J. Elith, J. Leathwick, 2006 .

REJOINDER TO "LEAST ANGLE REGRESSION" BY EFRON ET AL.

R. Tibshirani, T. Hastie, B. Efron, 2004, math/0406474.

Lasso and Elastic-Net Regularized Generalized Linear Models [R package glmnet version 4.0-2]

R. Tibshirani, T. Hastie, J. Friedman, 2020 .

Generalized additive models for medical research

R. Tibshirani, T. Hastie, 1995, Statistical methods in medical research.

CONFOUNDER ADJUSTMENT IN MULTIPLE HYPOTHESIS TESTING.

T. Hastie, A. Owen, Qingyuan Zhao, 2015, Annals of statistics.

Bias correction in species distribution models: pooling survey and collection data for multiple species

T. Hastie, William Fithian, J. Elith, 2014, Methods in ecology and evolution.

Finite-Sample Equivalence in Statistical Models for Presence-Only Data.

T. Hastie, William Fithian, 2012, The annals of applied statistics.

Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse cohort

Trevor Hastie, Anna Shcherbina, Euan A. Ashley, 2016 .

Imputing Missing Data for Gene Expression Arrays

Trevor Hastie, Robert Tibshirani, David Botstein, 2001 .

The Geometric Interpretation of Correspondence Analysis

Trevor Hastie, Michael Greenacre, T. Hastie, 1987 .

[Generalized Additive Models]: Rejoinder

Trevor Hastie, Robert Tibshirani, R. Tibshirani, 1986 .

Neural mechanisms of rhythm-based temporal prediction : Delta phase-locking reflects temporal predictability but not rhythmic entrainment

Trevor Hastie, Brian A. Wandell, Read More, 2017 .

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.

Trevor Hastie, Predrag Radivojac, Saurabh Baheti, 2016, American journal of human genetics.

Response to Mease and Wyner, Evidence Contrary to the Statistical View of Boosting, JMLR 9:131-156, 2008

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2008 .

Best Subset, Forward Stepwise or Lasso? Analysis and Recommendations Based on Extensive Comparisons

Trevor Hastie, Robert Tibshirani, Ryan J. Tibshirani, 2020 .

Gene expression patterns in ovarian carcinomas.

Trevor Hastie, Robert Tibshirani, David Botstein, 2003, Molecular biology of the cell.

Gene Expression Programs in Response to Hypoxia: Cell Type Specificity and Prognostic Significance in Human Cancers

Trevor Hastie, Michael T Longaker, Edwin H Rodriguez, 2006, PLoS medicine.

A Unified Near-Optimal Estimator For Dimension Reduction in l_α(0

Trevor Hastie, Ping Li, T. Hastie, 2007, NIPS 2007.

Statistical Models for Presence-Only Data: Finite-Sample Equivalence and Addressing Observer Bias

Trevor Hastie, William Fithian, T. Hastie, 2012 .

Novel methods for the design and evaluation of marine protected areas in offshore waters

Trevor Hastie, Jane Elith, Atte Moilanen, 2008 .

Post-transplantation lymphoproliferative disease in heart and heart-lung transplant recipients: 30-year experience at Stanford University.

T. Hastie, J. Montoya, M. Perlroth, 2003, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

Generalized Additive Model

Trevor Hastie, Robert Tibshirani, R. Tibshirani, 2005 .

Characterization of heterotypic interaction effects in vitro to deconvolute global gene expression profiles in cancer

Trevor Hastie, T. Hastie, P. Brown, 2007, Genome Biology.

A Closer Look at the Deviance

Trevor Hastie, T. Hastie, 1987 .

A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank

R. Tibshirani, T. Hastie, M. Rivas, 2019, bioRxiv.

TITLE : A statistical explanation of MaxEnt for ecologists

Steven J. Phillips, T. Hastie, Miroslav Dudík, 2010 .

Sparse Quadratic Discriminant Analysis and Community Bayes

T. Hastie, Ya Le, 2014, 1407.4543.

Constrained ordination analysis with flexible response functions

T. Hastie, Mu Zhu, G. Walther, 2005 .

�-norm Support Vector Machines

R. Tibshirani, T. Hastie, S. Rosset, 2003 .

Discussion: Projection Pursuit

Trevor Hastie, Robert Tibshirani, R. Tibshirani, 1985 .

Regression Analysis of Multiple Protein Structures

Trevor J. Hastie, Douglas L. Brutlag, Thomas D. Wu, 1998, J. Comput. Biol..

The Elements of Statistical Learning

Robert Tibshirani, Trevor J. Hastie, Jerome H. Friedman, 2001 .

Detecting clinically meaningful biomarkers with repeated measurements: An illustration with electronic health records

T. Hastie, T. Assimes, W. Winkelmayer, 2015, Biometrics.

No increased mortality with early aortic aneurysm disease.

T. Hastie, B. Hill, R. Dalman, 2011, Journal of vascular surgery.

Postwar Statistical Inference and Methodology

Trevor Hastie, Bradley Efron, T. Hastie, 2021, Computer Age Statistical Inference, Student Edition.

Kernel Smoothing Methods

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

Inference After Model Selection

Trevor Hastie, Bradley Efron, T. Hastie, 2016 .

Regularization in Cox Frailty Models

Gerhard Tutz, Trevor Hastie, T. Hastie, 2016 .

Radiation-induced gene expression in human subcutaneous fibroblasts is predictive of radiation-induced fibrosis.

Trevor Hastie, Jens Overgaard, Jan Alsner, 2008, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

Microarray analysis of the transcriptional response to single or multiple doses of ionizing radiation in human subcutaneous fibroblasts.

T. Hastie, A. Børresen-Dale, J. Overgaard, 2005, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

Gene Shaving: a new class of clustering methods for expression arrays

Trevor Hastie, Robert Tibshirani, David Botstein, 2000 .

Gene Expression Programs of Human Smooth Muscle Cells: Tissue-Specific Differentiation and Prognostic Significance in Breast Cancers

T. Hastie, P. Brown, Sayan Mukherjee, 2007, PLoS genetics.

Estimation and prediction in sparse and unbalanced tables

T. Hastie, D. Pregibon, Qingyuan Zhao, 2017, 1703.02081.

Sparse Discriminant Analysis Line Clemmensen

T. Hastie, Bjarne Ersb, 2010 .

Cross-validation: what does it estimate and how well does it do it?

R. Tibshirani, T. Hastie, Stephen Bates, 2021, 2104.00673.

Critical Role of Type III Interferon in Controlling SARS-CoV-2 Infection in Human Intestinal Epithelial Cells

Michael E. Miller, Lindsay C. Page, Kevin S. Smith, 2020, Cell Reports.

Statistical Models in S

T. Hastie, J. Chambers, 1991 .

Extensions to other settings

Trevor Hastie, Robert Tibshirani, R. Tibshirani, 2017 .

Graphical Methods for Assessing Logistic Regression Models: Comment

Trevor Hastie, T. Hastie, 1984 .

High-Dimensional Problems: p N

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

Objective Bayes Inference and MCMC

Trevor Hastie, Bradley Efron, T. Hastie, 2021, Computer Age Statistical Inference, Student Edition.

Fused Lasso Latent Feature Model

Trevor Hastie, Robert Tibshirani, Nicholas Johnson, 2015 .

[Flexible Parsimonious Smoothing and Additive Modeling]: Discussion

Trevor Hastie, T. Hastie, 1989 .

Discovery of molecular subtypes in leiomyosarcoma through integrative molecular profiling

Trevor Hastie, Robert Tibshirani, Andrew H. Beck, 2009, Oncogene.

Empirical Bayes Estimation Strategies

Trevor Hastie, Bradley Efron, T. Hastie, 2021, Computer Age Statistical Inference, Student Edition.

Boosting Algorithms: Regularization, Prediction and Model Fitting. Comment.

Trevor Hastie, T. Hastie, 2007 .

Decreasing human body temperature in the United States since the Industrial Revolution

Trevor Hastie, Julie Parsonnet, Catherine Ley, 2019, bioRxiv.

Generalized Linear Models

Trevor Hastie, Daryl Pregibon, T. Hastie, 2017 .

Fused Lasso Latent Feature Model [R package FLLat version 1.2-1]

Trevor Hastie, Robert Tibshirani, Nicholas Johnson, 2017 .

Discussions of boosting papers, and rejoinders

Trevor Hastie, Peter J. Bickel, Robert Tibshirani, 2004 .

Rejoinder: Best Subset, Forward Stepwise or Lasso? Analysis and Recommendations Based on Extensive Comparisons

Trevor Hastie, Robert Tibshirani, Ryan J. Tibshirani, 2020 .

Risk factors for progressive cartilage loss in the knee: a longitudinal magnetic resonance imaging study in forty-three patients.

Trevor Hastie, Thomas P Andriacchi, Sandip Biswal, 2002, Arthritis and rheumatism.

Polygenic risk modeling with latent trait-related genetic components

Trevor Hastie, Robert Tibshirani, Yosuke Tanigawa, 2019, European Journal of Human Genetics.

Response transformation models

Trevor Hastie, Robert Tibshirani, R. Tibshirani, 2017 .

Handwritten Digit Recognition via Deformable Prototypes

Trevor Hastie, Robert Tibshirani, R. Tibshirani, 1994 .

Microelectrode recording revealing a somatotopic body map in the subthalamic nucleus in humans with Parkinson disease.

Trevor Hastie, Pantaleo Romanelli, T. Hastie, 2004, Journal of neurosurgery.

The Elements of Statistical Learning Theory

Trevor Hastie, Robert Tibshirani, R. Tibshirani, 2001 .

Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation

Trevor Hastie, T. Hastie, 2008 .

Computer Age Statistical Inference by Bradley Efron

Trevor Hastie, Bradley Efron, T. Hastie, 2016 .

Discussion: Multivariate Adaptive Regression Splines

Trevor Hastie, Robert Tibshirani, Andreas Buja, 1991 .

Support-Vector Machines and Kernel Methods

Trevor Hastie, Bradley Efron, T. Hastie, 2016 .

Generalized Matrix Factorization pdf

Trevor Hastie, David I. Warton, Francis K. C. Hui, 2020, ArXiv.

[Monotone Regression Splines in Action]: Comment

Trevor Hastie, Robert Tibshirani, R. Tibshirani, 1988 .

Smoothing in detail

Trevor Hastie, Robert Tibshirani, R. Tibshirani, 2017 .

Graphs and Model Selection

Trevor Hastie, Martin J. Wainwright, Robert Tibshirani, 2015 .

The Jackknife and the Bootstrap

Trevor Hastie, Bradley Efron, T. Hastie, 2016 .

Genetics of 35 blood and urine biomarkers in the UK Biobank

Trevor Hastie, Robert Tibshirani, Anna Shcherbina, 2020, Nature Genetics.

A Unified Near-Optimal Estimator For Dimension Reduction in lalpha(0 < alpha <= 2) Using Stable Random Projections

Trevor J. Hastie, Ping Li, T. Hastie, 2007, NIPS.

PathGPS: Discover shared genetic architecture using biobank data

T. Hastie, Q. Zhao, Zijun Gao, 2022, bioRxiv.

Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions

T. Hastie, T. Parrish, S. Mackey, 2021, Scientific Reports.

REJOINDER TO "LEAST ANGLE REGRESSION" BY EFRON ET AL.

R. Tibshirani, T. Hastie, B. Efron, 2004, math/0406474.

Cross-validation: what does it estimate and how well does it do it?

R. Tibshirani, T. Hastie, Stephen Bates, 2021, Journal of the American Statistical Association.

Least angle regression

R. Tibshirani, T. Hastie, B. Efron, 2004, math/0406456.

THE LASSO AND RELATED METHODS 2 1 The lasso and related methods

R. Tibshirani, T. Hastie, Jonathan E. Taylor, 2007 .

2 Regularization Methods for Grouped Variable Selection

Mee Young Park, T. Hastie, 2022 .

"Preconditioning" for feature selection and regression in high-dimensional problems

R. Tibshirani, T. Hastie, D. Paul, 2007, math/0703858.

Temporal dynamics of the multi-omic response to endurance exercise training across tissues

Michael E. Miller, Ronald J. Moore, Kevin S. Smith, 2022, bioRxiv.

Comment on "Support Vector Machines with Applications"

T. Hastie, Ji Zhu, 2006, math/0612824.

Estimating the number of clusters in a data set via the gap statistic

R. Tibshirani, T. Hastie, G. Walther, 2000 .

RbX: Region-based explanations of prediction models

T. Hastie, Ismael Lemhadri, Harrison H. Li, 2022, ArXiv.

Research and Applications Synergistic drug combinations from electronic health records and gene expression

Maya B. Mathur, Yen S. Low, Aaron C. Daugherty, 2017 .

Temporal dynamics of the multi-omic response to endurance exercise training across tissues

Michael E. Miller, Ronald J. Moore, Kevin S. Smith, 2022, bioRxiv.

Regression with an ordered categorical response.

T. Hastie, C. Schnitzler, T J Hastie, 1989, Statistics in medicine.

Large-Scale Hypothesis Testing and FDRs

T. Hastie, B. Efron, 2021, Computer Age Statistical Inference, Student Edition.

A statistical explanation of MaxEnt for ecologists

Trevor Hastie, Miroslav Dudík, Jane Elith, 2011 .

DINA: Estimating Heterogenous Treatment Effects in Exponential Family and Cox Model

T. Hastie, Zijun Gao, 2021 .

Estimating Heterogeneous Treatment Effects for General Responses

Trevor Hastie, Zijun Gao, T. Hastie, 2021, 2103.04277.

Computer Age Statistical Inference: Algorithms, Evidence, and Data Science

Trevor Hastie, Bradley Efron, T. Hastie, 2016 .

Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities.

Madalina Fiterau, Scott L Delp, Jennifer L Hicks, 2018, Journal of biomechanics.

Spectral Regularization Algorithms for Learning Large Incomplete Matrices

Robert Tibshirani, Trevor J. Hastie, Rahul Mazumder, 2010, J. Mach. Learn. Res..

Weighted Low Rank Matrix Approximation and Acceleration pdf

Trevor Hastie, Elena Tuzhilina, T. Hastie, 2021, ArXiv.

Fast Numerical Optimization for Genome Sequencing Data in Population Biobanks

R. Tibshirani, T. Hastie, M. Rivas, 2021, bioRxiv.

Fast Numerical Optimization for Genome Sequencing Data in Population Biobanks

R. Tibshirani, T. Hastie, M. Rivas, 2021, bioRxiv.

Survival analysis on rare events using group-regularized multi-response Cox regression

Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2020, Bioinform..

A working guide to boosted regression trees.

J Elith, J R Leathwick, T Hastie, 2008, The Journal of animal ecology.

Generalized linear and generalized additive models in studies of species distributions: setting the scene

Trevor Hastie, Antoine Guisan, Thomas C. Edwards, 2002 .

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study

T. Hastie, S. Basu, N. Lo, 2020, BMC Medicine.

Healthcare worker absenteeism, child care costs, and COVID-19 school closures: a simulation analysis

T. Hastie, S. Basu, N. Lo, 2020 .

Ridge Regularization: An Essential Concept in Data Science

Trevor Hastie, T. Hastie, 2020, Technometrics.

Construction of longitudinal prediction targets using semisupervised learning

Booil Jo, L Eugene Arnold, Trevor J Hastie, 2018, Statistical methods in medical research.

Targeted use of growth mixture modeling: a learning perspective

Booil Jo, L Eugene Arnold, Trevor J Hastie, 2017, Statistics in medicine.

False Variable Selection Rates in Regression

R. Tibshirani, T. Hastie, Max G'Sell, 2013 .

Submitted to the Annals of Applied Statistics FALSE VARIABLE SELECTION RATES IN REGRESSION By

Trevor Hastie, Robert Tibshirani, R. Tibshirani, 2013 .

LARGE-SCALE MULTIVARIATE SPARSE REGRESSION WITH APPLICATIONS TO UK BIOBANK.

R. Tibshirani, T. Hastie, M. Rivas, 2022, The annals of applied statistics.

Large-Scale Sparse Regression for Multiple Responses with Applications to UK Biobank

Trevor Hastie, Robert Tibshirani, Ruilin Li, 2020, bioRxiv.

Combinatorial Extracellular Matrix Microenvironments for Probing Endothelial Differentiation of Human Pluripotent Stem Cells

T. Hastie, Luqia Hou, Maureen Wanjare, 2017, Scientific Reports.

Fast Lasso method for Large-scale and Ultrahigh-dimensional Cox Model with applications to UK Biobank

Trevor Hastie, Robert Tibshirani, Ruilin Li, 2020, bioRxiv.

Longitudinal data analysis using matrix completion pdf

Trevor J. Hastie, Lukasz Kidzinski, T. Hastie, 2018, ArXiv.

Modeling longitudinal data using matrix completion

T. Hastie, Lukasz Kidzi'nski, 2018, Journal of Computational and Graphical Statistics.

Confidence intervals for random forests: the jackknife and the infinitesimal jackknife

Trevor J. Hastie, Bradley Efron, Stefan Wager, 2013, J. Mach. Learn. Res..

TITLE: A working guide to boosted regression trees

Trevor Hastie, Jane Elith, John R. Leathwick, 2008 .

Clinically Relevant Molecular Subtypes in Leiomyosarcoma

Trevor Hastie, Andrew H. Beck, Xiangqian Guo, 2015, Clinical Cancer Research.

A Fast and Flexible Algorithm for Solving the Lasso in Large-scale and Ultrahigh-dimensional Problems

Trevor Hastie, Robert Tibshirani, Yosuke Tanigawa, 2019 .

Principal curve approaches for inferring 3D chromatin architecture

Trevor J. Hastie, Mark R. Segal, Elena Tuzhilina, 2020, bioRxiv.

Principal curve approaches for inferring 3D chromatin architecture

T. Hastie, M. Segal, Elena Tuzhilina, 2020, bioRxiv.

Effect of long-term antibiotic use on weight in adolescents with acne.

T. Hastie, H. Luft, D. Contopoulos-Ioannidis, 2016, The Journal of antimicrobial chemotherapy.

FIRE: functional inference of genetic variants that regulate gene expression

Nicholas B. Larson, Trevor J. Hastie, Stephen B. Montgomery, 2017, Bioinform..

Regularized linear discriminant analysis and its application in microarrays.

Trevor Hastie, Robert Tibshirani, Yaqian Guo, 2007, Biostatistics.

Rejoinder: Linear Smoothers and Additive Models

R. Tibshirani, T. Hastie, A. Buja, 1989 .

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions

Trevor Hastie, Robert Tibshirani, Ramin Fallahzadeh, 2020, Nature Machine Intelligence.

Detection of Circulating Tumor DNA in Patients With Uterine Leiomyomas

T. Hastie, M. van de Rijn, Shirley X. Zhu, 2019, JCO precision oncology.

Significant sparse polygenic risk scores across 813 traits in UK Biobank

R. Tibshirani, T. Hastie, M. Rivas, 2022, PLoS genetics.

Applications of the lasso and grouped lasso to the estimation of sparse graphical models

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2010 .

Wearable sensors enable personalized predictions of clinical laboratory measurements

T. Hastie, S. Delp, L. Kidzinski, 2021, Nature Medicine.

A clinico-genomic analysis of soft tissue sarcoma patients reveals CDKN2A deletion as a biomarker for poor prognosis

T. Hastie, M. van de Rijn, G. Frampton, 2019, Clinical Sarcoma Research.

Optimization and evaluation of T7 based RNA linear amplification protocols for cDNA microarray analysis

T. Hastie, S. Jeffrey, A. Børresen-Dale, 2002, BMC Genomics.

Risk estimation of distant metastasis in node-negative, estrogen receptor-positive breast cancer patients using an RT-PCR based prognostic expression signature

Trevor Hastie, Paul Cane, Hongyue Dai, 2008, BMC Cancer.

Using aggregate patient data at the bedside via an on-demand consultation service

N. Shah, Keith E. Morse, A. Callahan, 2021, medRxiv.

1-norm Support Vector Machines

Robert Tibshirani, Ji Zhu, Trevor J. Hastie, 2003, NIPS.

�-norm Support Vector Machines

R. Tibshirani, T. Hastie, S. Rosset, 2003 .

An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

Mark M. Davis, R. Tibshirani, T. Hastie, 2021, Nature Aging.

Causal Interpretations of Black-Box Models

Trevor Hastie, Qingyuan Zhao, T. Hastie, 2021, Journal of business & economic statistics : a publication of the American Statistical Association.

Feature Extraction for Nonparametric

T. Hastie, Mu Zhu, 2016 .

Assessing the significance of global and local correlations under spatial autocorrelation: A nonparametric approach

Trevor Hastie, Rahul Mazumder, Júlia Viladomat, 2014, Biometrics.

Feature Extraction for Nonparametric Discriminant Analysis

Trevor Hastie, Mu Zhu, T. Hastie, 2003 .

Principal component analysis

T. Hastie, P. Groenen, M. Greenacre, 2022, Nature Reviews Methods Primers.

Regularized Discriminant Analysis and Its Application in Microarrays

Trevor Hastie, Robert Tibshirani, Yaqian Guo, 2004 .

Linear Methods for Regression

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2001 .

Combinatorial extracellular matrix microenvironments promote survival and phenotype of human induced pluripotent stem cell-derived endothelial cells in hypoxia.

T. Hastie, Luqia Hou, N. Huang, 2016, Acta biomaterialia.

Proteomic analysis of monolayer-integrated proteins on lipid droplets identifies amphipathic interfacial α-helical membrane anchors

Joshua E. Elias, T. Hastie, B. Efron, 2018, Proceedings of the National Academy of Sciences.

Shrinking Trees

T. Hastie, D. Pregibon, 1990 .

[Local Regression: Automatic Kernel Carpentry]: Rejoinder

T. Hastie, C. Loader, 1993 .

Does cancer risk affect health-related quality of life in patients with Barrett's esophagus?

T. Hastie, M. Goldstein, L. Gerson, 2007, Gastrointestinal endoscopy.

Patient-Derived Health State Utilities for Gastroesophageal Reflux Disease

T. Hastie, M. Goldstein, G. Triadafilopoulos, 2005, The American Journal of Gastroenterology.

Combining biological gene expression signatures in predicting outcome in breast cancer: An alternative to supervised classification.

Trevor Hastie, Howard Y. Chang, Howard Y Chang, 2008, European journal of cancer.

Automatic Smoothing Spline Projection Pursuit

Trevor Hastie, Charles B. Roosen, T. Hastie, 1994 .

New cutpoints to identify increased HER2 copy number: analysis of a large, population-based cohort with long-term follow-up

T. Hastie, R. West, S. Leung, 2008, Breast Cancer Research and Treatment.

A tissue atlas of ulcerative colitis revealing evidence of sex-dependent differences in disease-driving inflammatory cell types and resistance to TNF inhibitor therapy

John T. Chang, Garry P. Nolan, R. Tibshirani, 2023, Science advances.

An Inflammatory Clock Predicts Multi-morbidity, Immunosenescence and Cardiovascular Aging in Humans

R. Tibshirani, T. Hastie, S. Shen-Orr, 2019, bioRxiv.

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study

T. Hastie, S. Basu, N. Lo, 2020, medRxiv.

CATS regression – a model‐based approach to studying trait‐based community assembly

Trevor Hastie, Bill Shipley, David I. Warton, 2015 .

Overview of Supervised Learning

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2001 .

Margin-constrained random projections and very sparse random projections

Kenneth Ward Church, Trevor Hastie, Perry Y. Li, 2006 .

ISSUES IN SEMI-PARAMETRIC REGRESSION WITH APPLICATIONS IN TIME SERIES MODELS FOR AIR POLLUTION AND MORTALITY

Trevor Hastie, Francesca Dominici, Aidan McDermott, 2003 .

Effective degrees of freedom: a flawed metaphor.

Lucas Janson, William Fithian, Trevor J Hastie, 2013, Biometrika.

Making better biogeographical predictions of species’ distributions

Trevor Hastie, Antoine Guisan, Anthony Lehmann, 2006 .

Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury

T. Hastie, T. Parrish, M. Wintermark, 2019, Scientific Reports.

Sparse EEG/MEG source estimation via a group lasso

Trevor Hastie, Anthony M Norcia, Benoit R Cottereau, 2017, PloS one.

NEWOPPORTUNITIESAT THE INTERFACEBETWEENECOLOGYANDSTATISTICS Point processmodels for presence-only analysis

Steven J. Phillips, T. Hastie, J. Elith, 2015 .

An exploration of sentiment summarization

Trevor Hastie, Christopher D. Manning, Shivakumar Vaithyanathan, 2003 .

LOCAL CASE-CONTROL SAMPLING: EFFICIENT SUBSAMPLING IN IMBALANCED DATA SETS.

Trevor Hastie, William Fithian, T. Hastie, 2013, Annals of statistics.

Scalable logistic regression with crossed random effects

Trevor Hastie, Art B. Owen, Swarnadip Ghosh, 2021, Electronic Journal of Statistics.

Learning interactions through hierarchical group-lasso regularization

Trevor Hastie, T. Hastie, Michael Lim, 2013, 1308.2719.

Non‐Parametric Logistic and Proportional Odds Regression

Trevor Hastie, Robert Tibshirani, R. Tibshirani, 1987 .

Multiclass-penalized logistic regression

T. Hastie, D. Nibbering, 2021, Computational Statistics & Data Analysis.

Representing cyclic human motion using functional analysis

Michael J. Black, Trevor J. Hastie, Hedvig Kjellström, 2005, Image Vis. Comput..

Linear Methods for Classification

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2001 .

LinCDE: Conditional Density Estimation via Lindsey's Method

Trevor Hastie, Zijun Gao, T. Hastie, 2021, J. Mach. Learn. Res..

A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression

Trevor Hastie, Noah Simon, Jerome Friedman, 2013, 1311.6529.

Human amygdala engagement moderated by early life stress exposure is a biobehavioral target for predicting recovery on antidepressants

T. Hastie, L. Williams, C. Nemeroff, 2016, Proceedings of the National Academy of Sciences.

Averaged gene expressions for regression.

Trevor Hastie, Robert Tibshirani, Mee Young Park, 2007, Biostatistics.

Usefulness of generalized additive models

T. Hastie, Ian Campbell, Robert Tibshiram, 1996, Statistical Methods in Medical Research.

Assessment of heterogeneous treatment effect estimation accuracy via matching

Trevor Hastie, Robert Tibshirani, Zijun Gao, 2020, Statistics in medicine.

The Elements of Statistical Inference

Trevor Hastie, Robert Tibshirani, Joel Friedman, 2001 .

Relating whole-brain functional connectivity to self-reported negative emotion in a large sample of young adults using group regularized canonical correlation analysis

Trevor J. Hastie, Matthew F. Glasser, Leanne M. Williams, 2021, NeuroImage.

The importance of transparency and reproducibility in artificial intelligence research

Trevor Hastie, Joelle Pineau, Robert Tibshirani, 2020, 2003.00898.

Support Vector Machines and Flexible Discriminants

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

Clustering microarray data

Trevor Hastie, Robert Tibshirani, Hugh A. Chipman, 2003 .

Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset

Trevor Hastie, Robert Tibshirani, Ken Jung, 2017, 1711.00083.

Use of a modified Michaelis-Menten equation to estimate growth from birth to 3 years in healthy full term babies

T. Hastie, J. Parsonnet, C. Ley, 2023, Research square.

Learning the Structure of Mixed Graphical Models

Trevor Hastie, Jason D. Lee, T. Hastie, 2015, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

Reorienting Latent Variable Modeling for Supervised Learning.

T. Hastie, E. Youngstrom, R. Findling, 2023, Multivariate behavioral research.

Gene expression profiling of low-grade endometrial stromal sarcoma indicates fusion protein-mediated activation of the Wnt signaling pathway.

T. Hastie, M. van de Rijn, L. Kidzinski, 2018, Gynecologic oncology.

Gene expression pro fi ling of low-grade endometrial stromal sarcoma indicates fusion protein-mediated activation of the Wnt signaling pathway

T. Hastie, M. Rijn, L. Kidzinski, 2018 .

Computer Age Statistical Inference, Student Edition

T. Hastie, B. Efron, 2021 .

Additive Models, Trees, and Related Methods

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

The mobilize center: an NIH big data to knowledge center to advance human movement research and improve mobility

Jure Leskovec, Christopher Ré, Trevor J. Hastie, 2015, J. Am. Medical Informatics Assoc..

Inference from presence-only data; the ongoing controversy.

Trevor Hastie, Will Fithian, T. Hastie, 2013, Ecography.

Posttransplantation lymphoproliferative disease in heart and heart-lung transplant recipients: thirty years experience at our hospital.

T. Hastie, J. Montoya, M. Perlroth, 2001, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

Physical activity is associated with changes in knee cartilage microstructure.

S L Delp, G E Gold, T. Hastie, 2018, Osteoarthritis and cartilage.

Combinatorial Extracellular Matrix Microenvironments for Probing Endothelial Differentiation of Human Pluripotent Stem Cells

T. Hastie, Luqia Hou, Maureen Wanjare, 2017, Scientific Reports.

Basis Expansions and Regularization

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2001 .

High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality

Gregory Piatetsky-Shapiro, Elder, A. G. Flesia, 2000 .

Elastic Net Regularization Paths for All Generalized Linear Models

Trevor Hastie, J. Kenneth Tay, Balasubramanian Narasimhan, 2021, J. Stat. Softw..

Neural networks and Deep Learning

T. Hastie, B. Efron, B. Adcock, 2016, Machine Learning Guide for Oil and Gas Using Python.

Statistical Curve Models For Inferring 3D Chromatin Architecture

T. Hastie, Mark Segal, Elena Tuzhilina, 2022, bioRxiv.

Generalized Additive Models

Stefan Sperlich, Jiérí Zelinka, T. Hastie, 2014 .

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

R. Tibshirani, T. Hastie, J. Friedman, 2004 .

Generalized Additive Models: Some Applications

R. Tibshirani, T. Hastie, Robert Tibshirani, 1987 .

Synergistic drug combinations from electronic health records and gene expression

Maya B. Mathur, Trevor J. Hastie, Nigam H. Shah, 2016, J. Am. Medical Informatics Assoc..

Coronary risk assessment among intermediate risk patients using a clinical and biomarker based algorithm developed and validated in two population cohorts

R. Tibshirani, T. Hastie, P. Tsao, 2012, Current medical research and opinion.

Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays

Francis K. C. Hui, T. Hastie, L. Kidzinski, 2020, J. Mach. Learn. Res..

DNA Microarrays and Gene Expression - From Experiments to Data Analysis and Modeling

Pierre Baldi, G. Wesley Hatfield, G. W. Hatfield, 2002 .

A Statistical View of Column Subset Selection

T. Hastie, Anav Sood, 2023, ArXiv.

One sketch for all: Theory and Application of Conditional Random Sampling

Kenneth Ward Church, Trevor J. Hastie, Ping Li, 2008, NIPS.

Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections pdf

Kenneth Ward Church, Trevor J. Hastie, Ping Li, 2006, J. Mach. Learn. Res..

Temporal dynamics of the multi-omic response to endurance exercise training across tissues

Michael E. Miller, Ronald J. Moore, Ian R. Lanza, 2023, bioRxiv.

A Proportional Observer Bias Model for Multispecies Distribution Modeling

T. Hastie, William Fithian, J. Elith, 2014 .

A New Algorithm for Matched Case-Control Studies with Applications to Additive Models

T. Hastie, D. Pregibon, 1988 .

A Sketch-based Sampling Algorithm on Sparse Data

Kenneth Ward Church, T. Hastie, Ping Li, 2006 .

Components of genetic associations across 2 , 138 1 phenotypes in the UK Biobank highlight novel adipocyte 2 biology 3 4

Christopher M. DeBoever, T. Hastie, M. Rivas, 2019 .

Cortisol and behavior in fragile X syndrome

T. Hastie, A. Reiss, M. Gunnar, 2002, Psychoneuroendocrinology.

Extracting cell-type-specific gene expression differences from complex tissues (144.30)

Mark M. Davis, R. Tibshirani, T. Hastie, 2010, The Journal of Immunology.

Missing value estimation methods for DNA microarrays

R. Tibshirani, T. Hastie, D. Botstein, 2001, Bioinform..

Regularization and variable selection via the elastic net

H. Zou, T. Hastie, 2005 .

Generalized Additive Models

T. Hastie, R. Tibshirani, 1986 .

Backfitting for large scale crossed random effects regressions

T. Hastie, A. Owen, Swarnadip Ghosh, 2020, The Annals of Statistics.

Saturating Splines and Feature Selection

Stephen P. Boyd, Michael I. Jordan, T. Hastie, 2016, Journal of machine learning research.

Using multivariate adaptive regression splines to predict the distributions of New Zealand's freshwater diadromous fish

T. Hastie, J. Elith, J. Leathwick, 2005 .

Degrees‐of‐freedom tests for smoothing splines

Trevor Hastie, Eva Cantoni, T. Hastie, 2002 .

BACKFITTING FOR LARGE SCALE CROSSED RANDOM EFFECTS REGRESSIONS BY SWARNADIP GHOSH*,

T. Hastie, Robin Owen, B., 2022 .

Generalized Additive Models.

R. Tibshirani, T. Hastie, R. Brown, 1991 .

Quantitative determination of age-related geometric changes in the normal abdominal aorta.

T. Hastie, D. Paik, G. Rubin, 2001, Journal of vascular surgery.

Generalized Additive Models

T. Hastie, R. Tibshirani, 1986 .

Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities.

T. Hastie, S. Delp, J. Hicks, 2018, Journal of biomechanics.

Surprises in High-Dimensional Ridgeless Least Squares Interpolation

T. Hastie, R. Tibshirani, A. Montanari, 2019, Annals of statistics.

An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

Mark M. Davis, R. Tibshirani, T. Hastie, 2021, Nature Aging.

Molecular Transducers of Physical Activity Consortium (MoTrPAC): Mapping the Dynamic Responses to Exercise

T. Hastie, S. Sealfon, B. Balliu, 2020, Cell.

1-norm Support Vector Machines

R. Tibshirani, T. Hastie, Ji Zhu, 2003, NIPS.

The elements of statistical learning: data mining, inference, and prediction, 2nd Edition

R. Tibshirani, T. Hastie, J. Friedman, 2020 .

The Graphical Lasso: New Insights and Alternatives

Trevor Hastie, Rahul Mazumder, T. Hastie, 2011, Electronic journal of statistics.

Monographs on statistics and applied probability

Susan A. Murphy, Niels Keiding, Richard L. Smith, 1990 .

Canonical correlation analysis in high dimensions with structured regularization

T. Hastie, L. Tozzi, Elena Tuzhilina, 2020, Statistical modelling.

Boosting and Support Vector Machines as Optimal Separators

Ji Zhu, Trevor J. Hastie, Saharon Rosset, 2003, IS&T/SPIE Electronic Imaging.

Discussion of “ Boosting Algorithms : Regularization , Prediction and Model Fitting ” by Peter Bühlmann and

T. Hastie, 2007 .

Learning interactions through hierarchical group-lasso regularization

T. Hastie, Michael Lim, 2013, 1308.2719.

Functional linear discriminant analysis for irregularly sampled curves

Gareth M. James, T. Hastie, SampledCurvesGareth M. JamesMarshall, 2001 .

Principal Curves

T. Hastie, W. Stuetzle, 2010 .

Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury

Max Wintermark, Sean Mackey, Marie Wasielewski, 2019, Scientific Reports.

Flexible Discriminant Analysis by Optimal Scoring

R. Tibshirani, T. Hastie, A. Buja, 1994 .

Generalized Additive Models

T. Hastie, R. Tibshirani, 1986 .