Keyword Search
Sessions Were Renumbered as of May 19.
Legend:
CC-W = McCormick Place Convention Center, West Building, CC-N = McCormick Place Convention Center, North Building
H = Hilton Chicago, UC= Conference Chicago at University Center
* = applied session ! = JSM meeting theme
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Keyword Search Criteria: Sparsity returned 36 record(s)
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Sunday, 07/31/2016
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Integrative Analysis of Transcriptomic and Metabolomic Data via Sparse Canonical Correlation Analysis with Incorporation of Biological Information
Sandra Safo, Emory University; Qi Long, Emory University
2:05 PM
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Estimating Longitudinal Covariance Structure in Functional Mapping of Quantitative Trait Loci
Ashwini Maurya, Michigan State University
2:35 PM
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Global-Local Shrinkage with Horseshoe Priors
Nicholas Polson, The University of Chicago
2:55 PM
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A Scalable Framework for Minimum Distance Estimation with Applications to Mixture Modeling and Robust, Structured Regression
Jocelyn Chi, North Carolina State University; Eric Chi, North Carolina State University
3:20 PM
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Graphlet Screening for High-Dimensional Variable Selection
Qi Zhang, University of Nebraska - Lincoln; Jiashun Jin, Carnegie Mellon University; Cun-Hui Zhang, Rutgers University
4:50 PM
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Monday, 08/01/2016
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Sparse Regression for Block Missing Data Without Imputation
Yufeng Liu, The University of North Carolina at Chapel Hill
8:35 AM
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Identifying Interactions Using Convex Optimization
Jacob Bien, Cornell University ; Robert Tibshirani, Stanford University; Noah Simon, University of Washington
10:55 AM
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Fast Sampling with Gaussian Scale-Mixture Priors
Anirban Bhattacharya, Texas A&M University
10:55 AM
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Graphical LASSO with Auxiliary Information: Application to Neural Connectivity
Giuseppe Vinci, Carnegie Mellon University; Robert Kass, Carnegie Mellon University; Valerie Ventura, Carnegie Mellon University; Matthew A. Smith, University of Pittsburgh
11:05 AM
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How Many Needles in the Haystack? Adaptive Inference and Uncertainty Quantification for the Horseshoe
Stephanie Van Der Pas; Botond Szabo, Leiden University; Aad van der Vaart, Leiden University
11:35 AM
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Semiparametric Inference via Sparsity-Induced Kriging for Massive Spatial Data Sets
Pulong Ma, University of Cincinnati; Emily Lei Kang, University of Cincinnati
11:50 AM
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Sparse Latent Class Regression for Multivariate Binary Data: A Bayesian Approach
Zhenke Wu, The Johns Hopkins University; Scott L. Zeger, The Johns Hopkins University
11:50 AM
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Some Properties of the One Group Prior for Sparse High-Dimensional Models
Jean-Bernard Salomond
11:55 AM
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Bayesian Multiple Testing Under Sparsity for Polynomial-Tailed Distributions
Xueying Tang, University of Florida; Ke Li, Southwestern University of Finance and Economics; Malay GHosh, University of Florida
2:05 PM
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Time Series Model Selection via Adpative Sparse Estimation
Seong-Tae Kim, North Carolina A&T State University; Kendra Kirby, North Carolina A&T State University
2:20 PM
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Bayesian Variable Selection for Skewed Heteroscedastic Response
Libo Wang, Florida State University; Yuanyuan Tang, Saint Luke's Health System; Debajyoti Sinha, Florida State University; Debdeep Pati, Florida State University; Stuart Lipsitz, Brigham and Women's Hospital
3:05 PM
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Tuesday, 08/02/2016
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Decouple/Recouple: Dynamic Graphical Modeling and Time Series Forecasting
Mike West, Duke University; Lutz Gruber, QuantCo
8:35 AM
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Two-Sample Tests for High-Dimensional Linear Regression with an Application to Detecting Interactions
Yin Xia, The University of North Carolina at Chapel Hill
8:35 AM
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Regularized LDA for High-Dimensional Data
Jeongyoun Ahn, University of Georgia; Yongho Jeon, Yonsei University
8:35 AM
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On the Optimality of Sliced Inverse Regression in High Dimensions
Qian Lin, Harvard; Xinran Li, Harvard; Jun S. Liu, Harvard
8:35 AM
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Hierarchical Sparse Modeling: A Choice of Two Regularizers
Xiaohan Yan, Cornell University; Jacob Bien, Cornell University
8:50 AM
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A General Theory of Hypothesis Tests and Confidence Regions for Sparse High-Dimensional Models
Yang Ning; Han Liu, Princeton
8:50 AM
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Stagewise Generalized Estimating Equations
Gregory Vaughan, University of Connecticut; Robert Aseltine, University of Connecticut Health Center; Kun Chen, University of Connecticut; Jun Yan, University of Connecticut
9:20 AM
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Tuning-free heterogeneity pursuit in massive networks
Zhao Ren, University of Pittsburgh; Yongjian Kang, University of Southern California; Yingying Fan, University of Southern California; Jinchi Lv, University of Southern California
9:35 AM
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Surrogate Aided Unsupervised Recovery of Sparse Signals in Single Index Models for Binary Outcomes Using Extreme Sampling
Abhishek Chakrabortty, Harvard; Tianxi Cai, Harvard
9:50 AM
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Variable Reduction in High-Dimensional Vector Time Series
Tucker McElroy, U.S. Census Bureau
10:35 AM
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A Meta-Analysis of Response Surface Studies
Byran Smucker, Miami University; Rebecca Ockuly, Miami University; Maria Weese, Miami University; David Edwards, Virginia Commonwealth University; Le Chang, Miami University
11:35 AM
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A Geometric Comparison of the Rejection Regions of Popular Tests in Genetic Epidemiology
Ian Barnett, Harvard; Xihong Lin, Harvard T.H. Chan School of Public Health; Zhonghua Liu, Harvard
2:30 PM
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Reconstruction of Directed Acyclic Graphs Networks Based on Prior Causal Ordering Information with Applications to Gene Regulatory Networks
Pei-Li Wang, University of Florida; George Michailidis, University of Florida
2:35 PM
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Sure Screening for Transelliptical Graphical Models
Yuxiang Xie, University of Washington; Chengchun Shi, North Carolina State University; Rui Song, North Carolina State University; Daniela Witten, University of Washington
2:45 PM
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Wednesday, 08/03/2016
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Incorporating Biological Information in Sparse Principal Component Analysis with Application to Genomic Data
Ziyi Li, Emory University; Qi Long, Emory University; Sandra Safo, Emory University
8:35 AM
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Sparse Seasonal and Periodic Vector Autoregressive Modeling
Vladas Pipiras, The University of North Carolina at Chapel Hill; Changryong Baek, Sungkyunkwan University; Richard A. Davis, Columbia University
8:55 AM
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Sparse Variable Selection Aiming at Minimum Prediction Error
Maarten Jansen, Université Libre de Bruxelles
9:00 AM
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Expandable Factor Analysis
Sanvesh Srivastava, University of Iowa; Barbara E. Engelhardt, Princeton; David Dunson, Duke University
11:05 AM
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Flexible Modeling of Local Dependence in Variables with a Natural Ordering
Guo Yu, Cornell University; Jacob Bien, Cornell University
2:50 PM
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Sparsity-Oriented Importance Learning
Chenglong Ye, University of Minnesota; Yi Yang, McGill University; Yuhong Yang, University of Minnesota
3:05 PM
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