Papers
My research is supported in part by the National Science Foundation (CAREER award DMS-1653017), the National Institutes of Health (R01 GM123993), and the Simons Foundation. Previously, my research was supported in part by a three-year grant from the National Science Foundation (NSF DMS-1405746).
Preprints
Ameer Dharamshi, Anna Neufeld, Keshav Motwani, Lucy L. Gao, Daniela Witten, Jacob Bien (2023) Generalized Data Thinning Using Sufficient Statistics [pdf]
Publications
Lucy Gao, Jacob Bien, and Daniela Witten (2022) Selective Inference for Hierarchical Clustering. Accepted, Journal of the American Statistical Association [pdf] [website] [software]
Sangwon Hyun, …, Jacob Bien (2022) Ocean Mover's Distance: Using Optimal Transport for Analyzing Oceanographic Data. Accepted, Proceedings of the Royal Society A [pdf] [code]
Sangwon Hyun, Mattias Rolf Cape, Francois Ribalet, and Jacob Bien (2023) Modeling Cell Populations Measured By Flow Cytometry With Covariates Using Sparse Mixture of Regressions. Annals of Applied Statistics 17(1), 357-377. [pdf] [software]
Daniel McDonald, Jacob Bien, et al. (2021) Can Auxiliary Indicators Improve COVID-19 Forecasting and Hotspot Prediction? Proceedings of the National Academy of Sciences 118(51) [pdf] [supplement] [code to reproduce all results]
Ines Wilms, Sumanta Basu, Jacob Bien, and David Matteson (2021) Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages. Accepted, Journal of the American Statistical Association [pdf] [software]
William Nicholson, Ines Wilms, Jacob Bien, and David Matteson (2020) High-Dimensional Forecasting via Interpretable Vector Autoregression. Journal of Machine Learning Research 21(166), 1−52. [pdf] [software]
Guo Yu, Jacob Bien, and Daniela Witten (2019) Discussion of “Covariate-assisted ranking and screening for large-scale two‐sample inference” Journal of the Royal Statistical Society, Series B. 81(2): 229-231. [paper] [supplement]
Jacob Bien, Irina Gaynanova, Johannes Lederer, and Christian Müller (2018) Prediction Error Bounds for Linear Regression With the TREX. TEST. 28, 451–474. [pdf]
Jacob Bien, Irina Gaynanova, Johannes Lederer, and Christian Müller (2016) Non-convex Global Minimization and False Discovery Rate Control for the TREX. Journal of Computational and Graphical Statistics. 27(1), 23-33. [pdf] [software]
William Nicholson, David Matteson, and Jacob Bien (2017) VARX-L: Structured Regularization for Large Vector Autoregressions with Exogenous Variables. International Journal of Forecasting. 33(3), 627-651 [pdf] [software]
Yin Lou, Jacob Bien, Rich Caruana, and Johannes Gehrke (2016) Sparse Partially Linear Additive Models. Journal of Computational and Graphical Statistics. 25(4), 1126-1140. [pdf] [software]
Jacob Bien, Florentina Bunea, and Luo Xiao (2016) Convex Banding of the Covariance Matrix. Journal of the American Statistical Association. 111(514), 834-845 [pdf] [software] [vignette]
Jacob Bien and Daniela Witten (2016) Penalized Estimation in Complex Models. In Bühlmann, Drineas, Kane, van der Laan (Eds.), Handbook of Big Data. Chapman and Hall/CRC
Reference.
[link]
Jacob Bien, Noah Simon, and Robert Tibshirani (2015) Convex Hierarchical Testing of Interactions. Annals of Applied Statistics. 9(1), 27-42. [pdf, supplement]
[software]
Jacob Bien, Jonathan Taylor, and Robert Tibshirani (2013) A Lasso for Hierarchical Interactions. Annals of Statistics.
41(3), 1111-1141 [pdf]
[software]
Robert Tibshirani, Jacob Bien, Jerome Friedman, Trevor Hastie,
Noah Simon, Jonathan Taylor, and Ryan Tibshirani (2012) Strong Rules for Discarding Predictors in Lasso-type Problems. Journal of the Royal Statistical Society, Series B. 74(2), 245-266
[pdf]
Neema Moraveji, Daniel Russell, Jacob Bien, David Mease (2011)
Measuring Improvement in User Search Performance Resulting from
Optimal Search Tips. Proceedings of SIGIR 2011.
[abstract]
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