Keyword Search
Legend:
CC = Vancouver Convention Centre F = Fairmont Waterfront Vancouver
* = applied session ! = JSM meeting theme
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Keyword Search Criteria: Dimension reduction returned 49 record(s)
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Sunday, 07/29/2018
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Sparse Functional Principal Component Analysis in a New Regression Framework
YUNLONG NIE, Simon Fraser University; Jiguo Cao, Simon Fraser University
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Envelope-Based Sparse Partial Least Squares
Guangyu Zhu, University of British Columbia; Zhihua Su, University of Florida
4:05 PM
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Think Before You Cluster: Testing for Clusterability
Naomi Brownstein, Florida State University; Margareta Ackerman, Santa Clara University; Andreas Adolfsson, Santa Clara University; Zachariah Neville, Florida State University
4:55 PM
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Nonlinear Multivariate Functional PCA
Jun Song, UNC Charlotte; Bing Li, The Pennsylvania State University
5:05 PM
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Monday, 07/30/2018
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Feature Selection and Classification Using Sparse Envelope Model
Minji Lee, University of Florida; Zhihua Su, University of Florida
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Statistical Techniques to Improve Random Projections and Other Similar Algorithms
Keegan Kang, Singapore University Of Technology And Design; Weipin Wong, Singapore University Of Technology and Design; Haikal Yeo, Independent
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Predictive Big Data Analytics in Mental Disorders Using the UK Biobank
Yiwang Zhou, University of Michigan; Ivo Dinov, Statistics Online Computational Resource, University of Michigan; Simeone Marino, Statistics Online Computational Resource, University of Michigan
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Factor Models for High-Dimensional Dynamic Networks: With Application to International Trade Flow Time Series 1981--2015
Elynn CHEN, Rutgers University; Rong Chen, Rutgers University
8:35 AM
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UNIQUE ENTITY ESTIMATION with APPLICATION to the SYRIAN CONFLICT
Beidi Chen, Rice University
9:15 AM
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New Approach to Dimention Reduction for Volatility of Stationary Multivariate Time Series"
Chung Eun Lee, University of Tennessee, Knoxville; Xiaofeng Shao, University of Illinois at Urbana-Champaign
9:35 AM
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Multilinear Low-Rank Vector Autoregressive Modeling via Tensor Decomposition
Di Wang, University of Hong Kong; Guodong Li, University of Hong Kong; Dr. LIAN Heng, City University of Hong Kong
10:35 AM
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Gradient-Based Approach to Sufficient Dimension Reduction for Functional and Longitudinal Data
Ming-Yueh Huang, Academia Sinica; Kwun Chuen Gary Chan, University of Washington
10:50 AM
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Coordinate-Independent Sparse Estimation in Semiparametric Models
Haileab Hilafu, University of Tennessee; Sandra Safo, University of Minnesota
10:50 AM
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Semi-Orthogonal Matrix Factorization
Yutong Li, University of Illinois at Urbana-Champaign; Ruoqing Zhu, University of Illinois Urbana-Champaign; Annie Qu, University of Illinois at Urbana-Champaign
11:35 AM
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Sufficient Dimension Reduction Using Deep Neural Networks
Yixi Xu, Purdue University; Xin Zhang, Florida State University; Xiao Wang , Purdue University
11:50 AM
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Correlation Tensor Decomposition and Its Application in Spatial Imaging Data
Yujia Deng, UIUC; Xiwei Tang, University of Virginia; Annie Qu, University of Illinois at Urbana-Champaign
12:05 PM
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A Weighted Learning Approach for Sufficient Dimension Reduction in Binary Classification
Seung Jun Shin, Korea University
2:20 PM
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A Semiparametric Approach to Model Effect Modification
Muxuan Liang, University of Wisconsin-Madison; Menggang Yu, University of Wisconsin-Madison
2:45 PM
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Tuesday, 07/31/2018
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Aggregated Pairwise Classification of Statistical Shapes with Optimal Points of Projection
Min Ho Cho, The Ohio State University; Sebastian Kurtek, The Ohio State University; Steve MacEachern, The Ohio State University
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An Alternative to the Carnegie Classifications: Using Structural Equation Models to Identify Similar Doctoral Institutions
Paul Harmon, Montana State University; Sarah McKnight, Montana State University; Laura Hildreth, Montana State University; Ian C. Godwin, Montana State University Office of Planning and Analysis; Mark Greenwood, Montana State University
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Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes Under Possible Non-Proportional Hazards
Lauren Spirko, Temple University; Karthik Devarajan, Fox Chase Cancer Center
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Application of a Method for Identifying Disease Subtypes That Are Etiologically Heterogeneous
Emily Zabor, Memorial Sloan Kettering Cancer Center; Colin B Begg, Memorial Sloan Kettering Cancer Center
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New Dimension Reduction Methods for Combining Longitudinally Measured Biomarkers
Ruth Pfeiffer , National Cancer Institute; Wei Wang , George Washington University ; Efstathia Bura , Vienna University of Technology
9:35 AM
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Application of a Method for Identifying Disease Subtypes That Are Etiologically Heterogeneous
Emily Zabor, Memorial Sloan Kettering Cancer Center; Colin B Begg, Memorial Sloan Kettering Cancer Center
9:55 AM
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Sparse Functional Principal Component Analysis in a New Regression Framework
Yunlong Nie, Simon Fraser University; Jiguo Cao, Simon Fraser University
10:05 AM
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High-Dimensional Sparse Generalized Eigenvalue Problem and Its Applications to Multivariate Statistics
Kean Ming Tan, University of Minnesota; Zhaoran Wang, Northwestern University; Han Liu, Northwestern University; Tong Zhang, Tencent Technology
10:50 AM
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Structural Learning and Integrative Decomposition of Multi-View Data
Irina Gaynanova, Texas A&M Univeristy; Gen Li, Columbia University
10:55 AM
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Aggregated Pairwise Classification of Statistical Shapes with Optimal Points of Projection
Min Ho Cho, The Ohio State University; Sebastian Kurtek, The Ohio State University; Steve MacEachern, The Ohio State University
10:55 AM
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Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes Under Possible Non-Proportional Hazards
Lauren Spirko, Temple University; Karthik Devarajan, Fox Chase Cancer Center
11:00 AM
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An Alternative to the Carnegie Classifications: Using Structural Equation Models to Identify Similar Doctoral Institutions
Paul Harmon, Montana State University; Sarah McKnight, Montana State University; Laura Hildreth, Montana State University; Ian C. Godwin, Montana State University Office of Planning and Analysis; Mark Greenwood, Montana State University
11:20 AM
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Bayesian Generalized Smoothing Spline ANOVA with Dimension Reduction
Chin-I Cheng, Central Michigan University; Paul Speckman, University of Missouri-Columbia
2:50 PM
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MASES: a Nonparametric Dimension Reduction Approach
Hui Zou, University of Minnesota; Qing Mai, Florida State University; Xin Zhang, Florida State University
3:20 PM
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Wednesday, 08/01/2018
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High-Dimensional Change Point Estimation via Sparse Projection
Tengyao Wang, University of Cambridge; Richard J Samworth, University of Cambridge
8:35 AM
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Imputed Factor Regression for High-Dimensional Block-Wise Missing Data
Yanqing Zhang, Yunnan University; Niansheng Tang, Yunnan University; Annie Qu, University of Illinois at Urbana-Champaign
8:50 AM
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Sparse Quadratic Classification Rules via Linear Dimension Reduction
Tianying Wang, Texas A & M University; Irina Gaynanova, Texas A&M Univeristy
10:35 AM
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Matching Using Sufficient Dimension Reduction for Causal Inference
Yeying Zhu, University of Waterloo; Wei Luo, Baruch College
10:55 AM
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Sparse Model Identification and Learning for Ultra-High-Dimensional Additive Partially Linear Models
Xinyi Li; Lily Wang, Iowa State University; Dan Nettleton, Iowa State University
11:05 AM
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A Robust and Efficient Approach to Causal Inference Based on Sparse Sufficient Dimension Reduction
Shujie Ma, UC Riverside-Dept of Statistics
11:15 AM
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Weighted Envelope Estimation to Handle Variability in Model Selection
Daniel J. Eck
11:20 AM
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Supervised Dimensionality Reduction for Exponential Family Data
Yoonkyung Lee, Ohio State University; Andrew Landgraf, Battelle Memorial Institute
11:35 AM
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The Two-To-Infinity Norm and Singular Subspace Geometry with Applications to High-Dimensional Statistics
Joshua Cape, Johns Hopkins; Dept. of Applied Math and Statistics; Minh Tang, Johns Hopkins University; Carey E Priebe, Johns Hopkins University
2:05 PM
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Optimal Quadratic Estimators Using Fourier Transform in the Central Subspaces
Jiaying Weng, University of Kentucky; Xiangrong Yin, University of Kentucky
2:20 PM
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On Post Dimension Reduction Statistical Inference
Kyongwon Kim, The Pennsylvania State University; Bing Li, The Pennsylvania State University
2:35 PM
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Applications of the Mixturegram for Determining the Number of Components in Finite Mixture Models
Chenlu Ke, University of Kentucky; Derek S. Young, University of Kentucky; Xiaoxue Zeng, Apple. Inc
2:35 PM
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Kernel-Based Nonlinear Dimension Reduction for Automatic Gender Classification
Katherine Kempfert, University of Florida; Yishi Wang, University of North Carolina Wilmington; Cuixian Chen, University of North Carolina Wilmington
2:50 PM
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Dimensional Analysis for Response Surface Methodology
Ching-Chi Yang, Penn State; Dennis Lin, Pennsylvania State University
2:50 PM
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Principal Weighted Support Vector Machines for Sufficient Dimension Reduction in Binary Classification
Hao Helen Zhang, University of Arizona
3:25 PM
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Dimension Reduction of High-Dimensional Data Sets Based on Stepwise SVM
Elizabeth Chou, National Chengchi University; Tzu-Wei Ko, National Chengchi University
3:35 PM
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