Publications

Publications

Pre-prints

Featured

Perry, Panigrahi, Bien, and Witten (2024) Inference on the proportion of variance explained in principal component analysis. arXiv:2402.16725

[paper] [software]

2024

Featured

Dharamshi, Neufeld, Motwani, Gao, Witten, and Bien (2024) Generalized data thinning using sufficient statistics. To appear in <i>Journal of the American Statistical Association</i>.

[paper] [software]

Neufeld, Dharamshi, Gao, and Witten (2024) Data thinning for convolution-closed distributions. To appear in <i>Journal of Machine Learning Research</i>.

[paper] [software]

Saha, Witten, and Bien (2024) Inferring independent sets of Gaussian variables after thresholding correlations. To appear in <i>Journal of the American Statistical Association</i>.

[paper] [software]

2023

Featured

James, Witten, Hastie, Tibshirani, and Taylor (2023) An Introduction to Statistical Learning, with Applications in Python. Springer Series in Statistics. 607 pages.

[amazon] [website]

Motwani and Witten (2023) Re-visiting inference after prediction. To appear in <i>Journal of Machine Learning Research</i>. arXiv:2306.13746

[paper] [software]

Zdeblick, Shea-Brown, Witten, and Buice (2023) Modeling functional cell types in spike train data. <i>PLoS Computational Biology</i> 19(10): e1011509.

[paper] [software]

Chen and Witten (2023) Selective inference for k-means clustering. <i>Journal of Machine Learning Research</i> 24(152): 1-41.

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Tan, Sun, and Witten (2023) Sparse reduced rank Huber regression in high dimensions. <i>Journal of the American Statistical Association</i> 118(544): 2383–2393.

Chen, Jewell, and Witten (2023) Quantifying uncertainty in spikes estimated from calcium imaging data. <i> Biostatistics</i> 24(2): 481-501.

[paper] [software]

2022

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Neufeld, Gao, Popp, Battle, and Witten (2022) Inference after latent variable estimation for single-cell RNA sequencing data. To appear in <i>Biostatistics</i>.

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Chen, Jewell, and Witten (2022) More powerful selective inference for the graph fused lasso. <i>Journal of Computational and Graphical Statistics</i> 32(2): 577-587.

[paper] [software]

Neufeld, Gao, and Witten (2022) Tree-values: Selective inference for regression trees. <i>Journal of Machine Learning Research</i>: 23(305): 1-43.

[paper] [software]

Gao, Bien, and Witten (2022) Selective inference for hierarchical clustering. To appear in <i>Journal of the American Statistical Association</i>.

[paper] [software]

Gao, Witten, and Bien (2022) Testing for association in multi-view network data. <i>Biometrics</i> 78(3): 1018-1030.

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Yu, Witten, and Bien (2022) Controlling costs: feature selection on a budget. <i>STAT</i> 11(1): e427.

Jewell, Fearnhead, and Witten (2022) Testing for a change in mean after changepoint detection. <i>Journal of the Royal Statistical Society, Series B</i> 84(4): 1082-1104.

[paper] [software]

2021

Featured

James, Witten, Hastie, and Tibshirani (2021) An Introduction to Statistical Learning, with Application in R, 2nd Edition. Springer Series in Statistics. 622 pages.

[website] [amazon]

Neufeld and Witten (2021) From two cultures to one. <i>Observational Studies</i> 7(1): 171-174.

Fleming, Jewell, Engelhard, Witten, and Witten (2021) Inferring spikes from calcium imaging in dopamine neurons. <i>PLoS ONE</i> 16(6): e0252345.

[paper] [software and data]

Nofallah, Mehta, Mercan, . . . and Shapiro. (2021) Machine learning techniques for mitoses classification. <i>Computerized Medical Imaging and Graphics</i> 87: 101832.

Zhao, Witten, and Shojaie (2021) In defense of the indefensible: a very naive approach to high-dimensional inference. <i>Statistical Science</i> 36(4): 562-577.

2020

Featured

Xia, Ma, Cui, Bzdok, Thirion, Bassett, Satterthwaite, Shinohara, Witten (2020) Multi-Scale Network Regression .... <i>Human Brain Mapping</i> 41: 2553-2566.

Shen, Gao, Witten, and Han (2020) Optimal estimation of variance in non-parametric regression with random design. <i>Annals of Statistics</i> 48(6): 3589-3618.

Shen, Han, and Witten (2020) Exponential inequalities for dependent V-statistics via random Fourier features. <i>Electronic Journal of Probability</i> 25(7): 1-18.

de Vries, Lecoq, Buice, . . . , Witten, . . . and Koch (2020) A large-scale, standardized physiological survey reveals higher order... <i>Nature Neuroscience</I> 23: 138-151.

Padilla, Sharpnack, Chen, and Witten (2020) Adaptive non-parametric regression with the K-NN fused lasso. <i>Biometrika</i> 107(2): 293-310.

Gao, Bien, and Witten (2020) Are clusterings of multiple data views independent? <i>Biostatistics</i> 21(4): 692–708.

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Jewell, Hocking, Fearnhead, and Witten (2020) Fast non-convex deconvolution of calcium imaging data. <i>Biostatistics</i> 21(4): 709–726.

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2019

Featured

Zdeblick, Shea-Brown, Witten, and Buice (2019). Data-driven discovery of functional cell types that improve models of neural activity. <i>NeurIPS Neuro-AI Workshop</i>.

Martin, Witten, and Willis (2019) Modeling microbial abundances and dysbiosis with beta-binomial regression. <i> Annals of Applied Statistics</i> 14(1): 94-115.

[paper] [software]

Rentzsch et al. (2019) CADD: Predicting the deleteriousness of variants throughout the human genome. <i>Nucleic Acids Research</i> 47(D1): D886- D894.

[paper] [software]

Wu and Witten (2019) Flexible and interpretable models for survival data. <i>Journal of Computational and Graphical Statistics</i> 28(4): 954-966.

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Yu, Bien, and Witten (2019) Discussion of “Covariate-assisted ranking and screening for large-scale ...” <i>Journal of the Royal Statistical Society, Series B</i> 81(2): 229-231.

Jewell and Witten (2019) Discussion of “Gene hunting with hidden Markov model knockoffs”. <i>Biometrika</i> 106(1): 23-26.

2018

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Petersen and Witten (2018) Data-adaptive additive modeling. <i>Statistics in Medicine</i> 38(4): 583-600.

Bis J et al. (2018) Whole exome sequencing study identifies novel rare and common Alzheimer’s-associated variants involved in ... <i>Molecular Psychiatry.</i>

Witten, Fertig, Anandkumar, and Dean (2018) What’s in a name? The need to nip NIPS. <i>NeurIPS Workshop on Critiquing and Correcting Trends in Machine Learning.</i>

Jewell and Witten (2018) Exact spike train inference via l0 optimization. <i>Annals of Applied Statistics</i> 12(4): 2457-2482.

[paper] [software]

Petersen, Simon, and Witten (2018) SCALPEL: Extracting neurons from calcium imaging data. <i>Annals of Applied Statistics</i> 12(4): 2430-2456.

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2017

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Inoue et al. (2017) A systematic comparison reveals substantial differences in chromosomal versus episomal encoding of enhancer activity. <i>Genome Research</i> 27(1): 38-52.

Morrison, Simon, and Witten (2017) Simultaneous detection and estimation of trait associations with genomic phenotypes. <i>Biostatistics</i> 18(1): 147-164.

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Chen, Shojaie, and Witten (2017) Network reconstruction from high-dimensional ordinary ... <i>Journal of the American Statistical Association</i> 112(520): 1697-1707.

Chen, Witten, and Shojaie (2017) Nearly assumptionless screening for the mutually-exciting multivariate Hawkes process. <i>Electronic Journal of Statistics</i> 11(1): 1207-1234.

2016

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Tan, Ning, Witten, and Liu (2016) Replicates in high dimensions, with applications to latent variable graphical models. <i>Biometrika</i> 103(4): 761-777.

Petersen, Simon, and Witten (2016) Convex regression with interpretable sharp partitions. <i>Journal of Machine Learning Research</i> 17(94): 1-31.

[paper] [software]

Sheng, Witten, and Zhou (2016) Hypothesis testing for differentially-correlated features. <i>Biostatistics</i> 17(4): 677-691.

Haris, Witten, and Simon (2016) Convex modeling of interactions with strong heredity. <i>Journal of Computational and Graphical Statistics</i> 25(4): 981-1004.

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Petersen, Witten, and Simon (2016) Fused lasso additive model. <i>Journal of Computational and Graphical Statistics</i> 25(4): 1005-1025.

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Bien and Witten (2016) Penalized Estimation in Complex Models. Handbook of Big Data. CRC Press. Editors: Peter Buhlmann, Michael Kane, Petros Drineas, and Mark van der Laan.

2015

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Tan and Witten (2015) Statistical properties of convex clustering. <i>Electronic Journal of Statistics</i> 9: 2324-2327.

Tan, Witten, and Shojaie (2015) The cluster graphical lasso for improved estimation of Gaussian graphical models. <i>Computational Statistics and Data Analysis</i> 85: 23-36.

Chen, Witten, and Shojaie (2015) Selection and estimation in mixed graphical models. <i>Biometrika</i> 102(1): 47-64.

2014

Featured

Tan, London, Mohan, Lee, Fazel, and Witten (2014) Learning graphical models with hubs. <i>Journal of Machine Learning Research</i> 15: 3297-3331.

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Zare ... Witten, Blau, and Noble (2014) Inferring clonal composition from multiple sections of a breast cancer. <i>PLOS Computational Biology</i> 10(7): e1003073.oble

Mohan, London, Fazel M, Witten D, and Lee (2014) Node-based learning of multiple Gaussian graphical models. <i>Journal of Machine Learning Research</i> 15: 445-488.

Tan and Witten (2014) Sparse biclustering of transposable data. <i>Journal of Computational and Graphical Statistics</i> 23(4): 985-1008.

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Voorman, Shojaie, and Witten (2014) Graph estimation with joint additive models. <i>Biometrika</i> 101(1): 85-101.

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Witten, Shojaie, and Zhang (2014) The cluster elastic net for high-dimensional regression with unknown variable grouping. <i>Technometrics</i> 56(1): 112-122.

Danaher, Wang, and Witten (2014) The joint graphical lasso for inverse covariance estimation ... <i>Journal of the Royal Statistical Society, Series B</i> 76(2): 373-397.

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Tan, Petersen, and Witten (2014) Classification of RNA-Seq Data. Statistical Analysis of Next Generation Sequencing Data. Springer. Editors: Daniel Nettleton and Somnath Data.

Kircher, Witten, et al. (2014) A general framework for estimating the relative pathogenicity of human genetic variants. <i>Nature Genetics</i> 46: 310-315.

[paper] [software]

Tay-Sontheimer et al. (2014) Detection of an endogenous urinary biomarker associated with CYP2D6 activity using global metabolomics. <i>Pharmacogenomics</i> 15(16): 1947-1962.

Grechkin et al. (2014) Pathway Graphical Lasso. <i>Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15).</i>

2013

Featured

James, Witten, Hastie, and Tibshirani (2013) An Introduction to Statistical Learning. <i>Springer Series in Statistics.</i> 426 pages.

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Witten (2013) Penalized unsupervised learning with outliers. <i>Statistics and its Interface</i> 6(2): 211-221.

Zhao et al. (2013) Is GATA3 expression maintained in regional metastases? A study of paired primary and metastatic ... <i>The American Journal of Surgical Pathology</i> 37(12): 1876- 1881.

Witten and Tibshirani (2013) Scientific research in the era of omics: the good, the bad, and the sloppy. <i>Journal of the American Medical Informatics Association</i> 20(1): 125-127.

2012

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Witten and Noble (2012) On the assessment of statistical significance of three-dimensional colocalization of sets of genomic elements. <i>Nucleic Acids Research</i> 40(9): 3849-3855.

Brunner et al. (2012) Transcriptional profiling of lncRNAs and novel transcribed region across a diverse panel of archived human cancers. <i>Genome Biology</i> 13(8):R75.

Williams, Witten, Duester, and Chou (2012) The benefits of implant removal from the foot and ankle. <i>Journal of Bone and Joint Surgery</i> 94(14): 1316-1320.

Patwardhan et al. (2012) Massively parallel functional dissection of mammalian enhancers in vivo. <i>Nature Biotechnology</i> 30(3): 265-270.

Mohan et al. (2012) Structured sparse learning of Gaussian graphical models. <i>Advances in Neural Information Processing Systems.</i>

Sung et al. (2012) Molecular signatures from omics data: from chaos to consensus. <i>Biotechnology Journal</i> 7(8): 946-957.

Micheel et al; Institute of Medicine (2012) Evolution of Translational Omics: Lessons Learned and the Path Forward. <i>National Academy of Sciences Press</i>. 300 pages.

[consensus report]

Maldonado et al. (2012) The Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT): A New Tool for the ... <i>Psychosomatics</i> 53(2): 123-132.

2011

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Li, Witten, Johnstone, and Tibshirani (2011) Normalization, testing, and false discovery rate estimation for RNA-sequencing data. <i>Biostatistics</i> 13(3): 523-538.

Clemmensen, Hastie, Witten, and Ersboll (2011) Sparse discriminant analysis. <i>Technometrics</i> 53(4): 406-413.

Witten (2011) Classification and clustering of sequencing data using a Poisson model. <i>Annals of Applied Statistics</i> 5(4): 406-413.

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Witten, Friedman, and Simon (2011) New insights and faster computations for the graphical lasso. <i>Journal of Computational and Graphical Statistics</i> 20(4): 892-900.

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Witten and Tibshirani (2011) Penalized classification using Fisher’s linear discriminant. <i>Journal of the Royal Statistical Society, Series B</i> 73(5): 753-772.

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Witten and Tibshirani (2011) Supervised multidimensional scaling for visualization, classification, and bipartite ranking. <i>Computational Statistics and Data Analysis</i> 55(1): 789-801.

Wu et al. (2011) Endogenous versus tumor-specific host response to breast carcinoma: a study of stromal response ... <i>Clinical Cancer Research</i> 17(3): 437-446.

Ganjoo et al. (2011) The prognostic value of tumor associated macrophages in leiomyosarcoma: A single institution study. <i>American Journal of Clinical Oncology</i> 34(1): 82-86.

2010

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Witten, Tibshirani, Gu, Fire, Lui (2010) Ultra-high throughput sequencing-based small RNA discovery ... <i>BMC Biology</i> 8(1): 58.

Witten and Tibshirani (2010) Survival analysis with high-dimensional covariates. <i>Statistical Methods in Medical Research</i> 19(1): 29-51.

Witten and Tibshirani (2010) A framework for feature selection in clustering. <i>Journal of the American Statistical Association</i> 105(490): 713-726.

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Caramuta et al. (2010) MicroRNA expression profiles associated with mutational status and survival in malignant melanoma. <i>Journal of Investigative Dermatology</i> 130: 2062-2070.

Jockers and Witten (2010) A comparative study of machine learning methods for authorship attribution. <i>Literary and Linguistic Computing</i> 25(2): 215-223.

Beck et al. (2010) Discovery of molecular subtypes in leiomyosarcoma through integrative molecular profiling. <i>Oncogene</i> 29: 845-854

Beck et al. (2010) 3’-end sequencing for expression quantification (3SEQ) from archival tumor samples. <i>PLoS ONE</i> 5(1): e8768.

2009

Featured

Witten, Hastie, and Tibshirani (2009) Discussion of “On consistency and sparsity of principal components ...” <i>Journal of the American Statistical Association</i> 104(486): 698-699.

Witten, Tibshirani, and Hastie (2009) A penalized matrix decomposition, with applications to canonical correlation analysis and principal components. <i>Biostatistics</i> 10(3): 515-534.

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Witten and Tibshirani (2009) Covariance-regularized regression and classification for ... <i>Journal of the Royal Statistical Society, Series B</i> 71(3): 615-636.

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Witten and Tibshirani (2009). Extensions of sparse canonical correlation analysis ... <i>Statistical Applications in Genetics and Molecular Biology</i> 8(1): Article 28.

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Somervaille et al. (2009) Hierarchical maintenance of MLL myeloid leukemia stem cells employs a transcriptional program shared ... <i>Cell Stem Cell</i> 4(2): 129-140.

Jockers, Witten, and Criddle (2009) Reassessing the authorship of the Book of Mormon ... <i>Literary and Linguistic Computing</i> 23(4): 465-491.

Brar et al. (2009) Breast angiosarcoma: case series and expression of vascular endothelial growth factor. <i>Case Reports in Oncology</i> 2: 242-250.

2008

Featured

Chou et al. (2008) Postoperative pain following foot and ankle surgery: a prospective study. <i>Foot and Ankle International</i> 29(11): 1064-1069

Witten and Tibshirani (2008) Testing significance of features by lassoed principal components. <i>Annals of Applied Statistics</i> 2(3): 986-1012.

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Huang et al. (2008) A recoding method to improve the humoral immune response to an HIV DNA vaccine. <i>PLoS ONE</i> 3(9): e3214.

Macpherson et al. (2008) Nonadaptive explanations for signatures of partial selective sweeps in Drosophila. <i>Molecular Biology and Evolution</i> 25(6): 1025-1042.